Implementing a CNN for Text Classification.ipynb

implementing-a-cnn-for-text-classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Implementing a CNN for Text Classification.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "4Yu_phF5Y6It"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/gist/nkcr/09dbf3145c202109d7128c0ee5983b71/implementing-a-cnn-for-text-classification.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "62R0bOctYP83",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "From http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/\n",
        "\n",
        "Version to compare with our simpler implementation. Here we keep all documents and use sigmoid.\n"
      ]
    },
    {
      "metadata": {
        "id": "HA_vXG1jYIyV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4Yu_phF5Y6It",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Data introspection"
      ]
    },
    {
      "metadata": {
        "id": "m08klqDGYRGm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 849
        },
        "outputId": "3baac188-215e-41a3-9bca-2e6d5f7f1fc4"
      },
      "cell_type": "code",
      "source": [
        "from nltk.corpus import reuters\n",
        "import nltk\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "nltk.download('reuters')\n",
        "\n",
        "fileids = reuters.fileids()\n",
        "trainids = list(filter(lambda doc: doc.startswith(\"train\"), fileids))\n",
        "testids = list(filter(lambda doc: doc.startswith(\"test\"), fileids))\n",
        "voc = set()\n",
        "train_voc = set()\n",
        "test_voc = set()\n",
        "\n",
        "\n",
        "#\n",
        "# Saving documents length to an array and voc to a set\n",
        "#\n",
        "doclens = []\n",
        "for fileid in fileids:\n",
        "  doclens.append(len(reuters.words(fileid)))\n",
        "  for word in reuters.words(fileid):\n",
        "    voc.add(word)\n",
        "\n",
        "trainlens = []\n",
        "for fileid in trainids:\n",
        "  trainlens.append(len(reuters.words(fileid)))\n",
        "  for word in reuters.words(fileid):\n",
        "    train_voc.add(word)\n",
        "\n",
        "testlens = []\n",
        "for fileid in testids:\n",
        "  testlens.append(len(reuters.words(fileid)))\n",
        "  for word in reuters.words(fileid):\n",
        "    test_voc.add(word)\n",
        "  \n",
        "#\n",
        "# Print histogram of documents lenght\n",
        "#\n",
        "plt.rcParams[\"figure.figsize\"] = (6,10)\n",
        "f, (ax1, ax2, ax3) = plt.subplots(3,1,sharex='all', sharey='all')\n",
        "ax1.hist(doclens, bins=100)\n",
        "ax1.set_title(\"Histogram of documents lengths\")\n",
        "ax2.hist(trainlens, bins=100)\n",
        "ax2.set_title(\"Histogram of  Train documents lengths\")\n",
        "ax2.set_ylabel(\"Number of documents\")\n",
        "ax3.hist(testlens, bins=100)\n",
        "ax3.set_title(\"Histogram of  Test documents lengths\")\n",
        "ax3.set_xlabel(\"Number of words\")\n",
        "plt.tight_layout(pad=0.4)\n",
        "plt.show()\n",
        "\n",
        "\n",
        "#\n",
        "# Print some infos\n",
        "#\n",
        "print(\"Number of documents: {}. Average len: {}. Median len: {}\"\n",
        "      .format(len(fileids), np.mean(doclens), np.median(doclens)))\n",
        "print(\"Number of Train documents: {}. Average len: {}. Median len: {}\"\n",
        "      .format(len(trainids), np.mean(trainlens), np.median(trainlens)))\n",
        "print(\"Number of Test documents: {}. Average len: {}. Median len: {}\"\n",
        "      .format(len(testids), np.mean(testlens), np.median(testlens)))\n",
        "print(\"Vocabulary size: {}. Train voc size: {}. Test voc size: {}\"\n",
        "      .format(len(voc), len(train_voc), len(test_voc)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package reuters to /content/nltk_data...\n",
            "[nltk_data]   Package reuters is already up-to-date!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbMAAALUCAYAAACB9Nz4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKd7/B4IUG0sn2ksNAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb6de0ea940>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Number of documents: 10788. Average len: 159.51992955135336. Median len: 109.0\n",
            "Number of Train documents: 7769. Average len: 161.37160509718112. Median len: 110.0\n",
            "Number of Test documents: 3019. Average len: 154.7548857237496. Median len: 106.0\n",
            "Vocabulary size: 41600. Train voc size: 35247. Test voc size: 22337\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "U8DjEJ0JfO4x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 692
        },
        "outputId": "60c23d17-9517-4244-c89e-f074ebae4538"
      },
      "cell_type": "code",
      "source": [
        "#\n",
        "# Saving number of categories\n",
        "#\n",
        "catnums = []\n",
        "single_cat = 0\n",
        "for fileid in fileids:\n",
        "  catnums.append(len(reuters.categories(fileid)))\n",
        "  if len(reuters.categories(fileid)) == 1:\n",
        "    single_cat = single_cat + 1\n",
        "\n",
        "traincattype = set()\n",
        "traincatnums = []\n",
        "single_train_cat = 0\n",
        "for fileid in trainids:\n",
        "  traincatnums.append(len(reuters.categories(fileid)))\n",
        "  for cat in reuters.categories(fileid):\n",
        "    traincattype.add(cat)\n",
        "  if len(reuters.categories(fileid)) == 1:\n",
        "    single_train_cat = single_train_cat + 1\n",
        "\n",
        "testcattype = set()\n",
        "testcatnums = []\n",
        "single_test_cat = 0\n",
        "for fileid in testids:\n",
        "  testcatnums.append(len(reuters.categories(fileid)))\n",
        "  for cat in reuters.categories(fileid):\n",
        "    testcattype.add(cat)\n",
        "  if len(reuters.categories(fileid)) == 1:\n",
        "    single_test_cat = single_test_cat + 1\n",
        "\n",
        "#\n",
        "# Print histogram of documents lenght\n",
        "#\n",
        "plt.rcParams[\"figure.figsize\"] = (6,10)\n",
        "f, (ax1, ax2, ax3) = plt.subplots(3,1,sharex='all', sharey='all')\n",
        "ax1.hist(catnums, bins=len(reuters.categories()))\n",
        "ax1.set_title(\"Histogram of number of categories\")\n",
        "ax2.hist(traincatnums, bins=len(reuters.categories()))\n",
        "ax2.set_title(\"Histogram of number of Train categories\")\n",
        "ax2.set_ylabel(\"Number of documents\")\n",
        "ax3.hist(testcatnums, bins=len(reuters.categories()))\n",
        "ax3.set_title(\"Histogram of number of Test categories\")\n",
        "ax3.set_xlabel(\"Number of categories\")\n",
        "plt.show()\n",
        "\n",
        "#\n",
        "# Print some infos\n",
        "#\n",
        "print(\"Number of categories: {}. Average num: {}. Median num: {}\"\n",
        "      .format(len(reuters.categories()), np.mean(catnums), np.median(catnums)))\n",
        "print(\"Number of Train categories: {}. Average num: {}. Median num: {}\"\n",
        "      .format(len(traincattype), np.mean(traincatnums), np.median(traincatnums)))\n",
        "print(\"Number of Test categories: {}. Average num: {}. Median num: {}\"\n",
        "      .format(len(testcattype), np.mean(testcatnums), np.median(testcatnums)))\n",
        "print((\"Doc with 1 cat: {} ({:.2f}%).\"\n",
        "        \"Train doc with 1 cat: {} ({:.2f}%). \"\n",
        "        \"Test doc with 1 cat: {} ({:.2f}%)\")\n",
        "      .format(single_cat, single_cat/len(fileids)*100, \n",
        "              single_train_cat, single_train_cat/len(trainids)*100, \n",
        "              single_test_cat, single_test_cat/len(testids)*100))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAJbCAYAAAD+AQM6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3Xt8TXei///X3rmURIKkCaMXpS1a\nDapKXTIlEUm0jkhRUqleZlpDtR7NccvIoHWJOjGK0KneHNNWWrcaR1FFhwpDY1KPHpyhM/1SJQm5\nEZHb5/eHnz3Ciu2SnWTzfj4eHo+9V9b+rPdeO/Jel73XthljDCIiIpew13YAERGpm1QQIiJiSQUh\nIiKWVBAiImJJBSEiIpZUECIiYkkFcQtr3bo1x48frzRt5cqVPPfccwD8+c9/Zu7cuVccIzMzkwMH\nDrgqokuVl5fz7LPPEhYWxsGDB2tsubt27SIiIqLGlrdp0yZ69OjB5MmTq2W8zz77rFrGuRpRUVHk\n5OTU2PKkMs/aDiB117Bhw5zOs2LFCh555BHatGlTA4mqV1ZWFrt37+b777/Hy8urtuO4zObNmxk4\ncCBjxoy54bGys7N57733GDx4cDUkc279+vU1shyxpj0IqdL8+fP5/e9/D8CXX37Jk08+SXR0NP36\n9WPXrl18+umnfPHFF8yePZsPP/yQiooK/vjHPxIVFUVUVBQTJkygqKgIgB9++IE+ffrQp08fFixY\n4Bjj6NGj9OjRgxkzZjgK6euvv6Zfv35ERkYSGxvL/v37gfNb3k8//TTTp08nPDyc2NhYMjMziY+P\np3v37sybN8/yeRw4cIAhQ4YQFRVF//792bZtG+Xl5cTHx1NRUUG/fv0u2wuaP38+b7zxBqNGjSI8\nPJyBAweSlZUFQFhYGHv27HHMe+H+heeyePFiIiMjiYyM5O9//zsvvfQSoaGhTJw4sdIyZs2aRWRk\nJFFRUWRkZABQUlLCtGnTiIyMJCwsjHfeeafSchYsWEBkZCTHjh2rNFZV637JkiVs2LCBZcuWMWnS\npMvWzV//+leeeOIJIiMjefnll8nLy7viazBkyBCOHTtGVFQUJSUlHDp0iGHDhhEZGUm/fv3Yt2+f\nI8+bb75J9+7dGTp0KO+++y7x8fEA5OXl8dprrxEZGUnfvn159913HXlat27Nn/70JyIjIykvL6+0\nl5uWlkZUVBRhYWG8/vrrFBcXA/C3v/2NAQMG0LdvX6Kjo/nyyy8tfw/kOhi5ZbVq1cr88ssvlaat\nWLHCDB8+3BhjzLx580xiYqIxxpguXbqYo0ePGmOM2b17t5kxY4Yxxphhw4aZ1atXG2OMWbt2rYmJ\niTFnzpwxZWVl5ne/+51JTU01xhgzYMAA8/HHHxtjjPnwww/NQw89ZHbu3GmOHDli2rZta1auXGmM\nMaa0tNR06tTJ7N271xhjzPz58x15du7cadq2bWt27txpKioqzFNPPWViY2NNUVGROXjwoHnwwQdN\ncXFxpedTXl5uoqOjzV/+8hdjjDHff/+9efTRR01hYaE5cuSIeeCBByzXzbx580zXrl3N0aNHTUVF\nhXnppZfMwoULjTHG9OrVy+zevdsx74X7R44cMQ8++KBZtWqVMcaY0aNHm549e5qTJ0+aU6dOmYce\nesj89NNPZufOneaBBx4wa9euNcYYk5aWZvr372+MMWbBggVm+PDh5ty5c+bMmTMmJibGbN682bGc\nSZMmWea90rofP3684/bFzpw5Yzp37mwOHjxojDFm2rRpZsqUKU5fg969ezvWbZ8+fcxnn31mjDFm\nz549pkePHqa0tNRs3rzZ9O7d25w+fdrk5uaaqKgoM2zYMGOMMUlJSSYpKckYY0xubq7p2bOnY322\natXKLFq0yJHxwu/o7t27TdeuXc3x48cdYyQnJxtjjImNjTW7du0yxhjzz3/+07z++uuW60iunfYg\nbnHx8fGOrc6oqCjmzJljOV9gYCDLli3j559/plOnTpdtDQNs3bqVmJgYfHx88PDwIDY2lm+//Zbi\n4mJ++OEHnnzySQCeeeYZzEVXeCktLXUck/f09GTHjh106NABgE6dOnHkyBHHvP7+/nTp0gWbzcb9\n999P586dqV+/Pvfffz/l5eWcOnWqUqajR4+Sk5PDE088AUBISAjNmjVzbOleSadOnbjjjjuw2Ww8\n8MAD/PLLL04fU1ZWRlRUFACtWrUiJCSEgIAAGjduTFBQkGMv5LbbbiM6OhqA6Oho9u/fz7lz59iy\nZQtxcXF4e3vj4+ND//792bhxo2P8nj17Wi63qnV/JRkZGTRt2pRWrVoBMHbsWCZOnOj0Nbjgxx9/\n5OTJkwwcOBCARx55hICAAPbu3cuePXvo2bMnvr6+NGrUyLH+Ab755hvi4uIAaNSoEREREZWyWj3H\nzZs307dvX5o0aQLA0KFDHeslMDCQ1atXc/jwYe655x5SUlKu+Lzl6ukcxC1u6dKlNG3a1HF/5cqV\nrFmz5rL5Fi1axKJFi4iNjeVXv/oViYmJdO7cudI8p06domHDho77DRs25OTJk+Tn52Oz2fD39wfA\ny8uLwMBAx3weHh40aNCgUqZVq1ZRUlJCSUkJNpvN8TNfX1/Hbbvdjo+PDwA2mw273U55efllmfz8\n/CqN4e/vz6lTp7jrrruuuG78/PwqZbx0bCseHh7Uq1fvsnyXjtGoUSPs9vPbZxeee35+PoWFhcyc\nOdNR1CUlJbRr184xxsXr99LnabXuryQ3N9fxmgB4e3s7bl/pNbigoKCA4uJiR9EBnD59mry8PAoK\nChx/zIFKt0+dOlVpuf7+/o7ihPPr5lKFhYV89dVXbN++HQBjDKWlpQDMmDGDRYsW8fzzz1OvXj1e\nf/11R0nLjVFByFW5++67mTlzJhUVFaxevZqEhAS2bdtWaZ7bb7/dcQwbzh9rvv3222nQoAHGGM6e\nPUv9+vUpKyu7bEv/goyMDBYvXsznn3/OnXfeybfffktSUtJ15w4MDCQ/Px9jjOOPXF5eXqWCulZ2\nu52KigrH/fz8/Gse4+LHFBQUAOf/MAYHB/PCCy/Qq1evaxqvqnV/JY0bNyY3N9dx/+zZs+Tn53Ps\n2LGreg2Cg4Px9fW1PJGckZHhOP8E509uX5q1WbNmV501ODiYAQMGMH78+Mt+dvvtt5OUlERSUhLb\nt29n9OjRhIaGVtqYkOujQ0zi1KlTp3j++ec5ffo0drud9u3bO/7Yenp6UlhYCJw/NLBmzRrOnj1L\nWVkZy5cv5/HHH8fX15d7773XcfIwLS3Ncov0wrICAwNp1qwZZ8+eZdWqVRQVFVU6JHUt7rzzTpo2\nbcq6deuA83+4cnJyKm2VX6ugoCDHSe1169Zx7ty5ax6juLiYr776CoANGzYQEhKCt7c34eHhfP75\n55SXl2OMYeHChfz1r391Ol5V6/5KHnnkEbKzs/n+++8BWLhwIampqVd8DTw9PSkqKqKsrIw77riD\npk2bOgri1KlTvP766xQVFRESEsLWrVspLi6moKCg0onjnj17kpaW5njMV199VeWhswvCwsLYuHGj\nY8Ni06ZNvPvuu5SWlhIfH+/YA2nbti2enp6OvTO5MdqDEKcCAgIIDQ3lqaeewsPDAy8vL6ZPnw5A\n7969mT17NkeOHGHChAkcPHiQ2NhYjDF06dKFZ599FoDJkyeTlJTE+++/T0xMDE2aNLEsidDQUD75\n5BN69+5NkyZNSExMJDMzk1dfffWq3nZ7KZvNxpw5c5g8eTILFiygfv36vP322/j4+FS5F+PMyJEj\nmTx5Mp999hmRkZHcd9991zxGy5Yt2bt3LykpKdjtdpKTkwGIi4vj6NGjPPHEExhjeOihhxg+fLjT\n8aKioqpc91WpX78+8+fPZ+zYsQA0b96c5ORkfH19q3wNZs6cScOGDenevTurVq1izpw5TJkyhblz\n52K323n++efx8fEhIiKCrVu3EhUVRfPmzYmOjiY9PR2AMWPGMGXKFKKiorDb7bz00ktOC7tt27aM\nGDHC8c6zwMBApk6dipeXFwMHDnR8dsdutzNp0iTq16/vdJ2JczZzvZtmItfo4sM8jz32GB999JFb\nfn5Crs7Fr/fHH3/Mjh07SE1NreVUci20HyY14tVXX2Xx4sUApKenY4zhnnvuqd1Q4jL79+8nPDyc\n/Px8ysrK2Lhxo+NdUeI+tAchNeLw4cNMnDiR/Px8vLy8GDt2rNNj5OLe5s2bxxdffIGHhwcdOnRg\n6tSpOvTjZlQQIiJiSYeYRETE0k31Lqbs7MLajnCZxo19yM0tcj5jHeBOWcG98rpTVnCvvO6UFepm\n3qAgP8vp2oNwMU9Pj9qOcNXcKSu4V153ygruldedsoJ75VVBiIiIJRWEiIhYUkGIiIglFYSIiFhS\nQYiIiCUVhIiIWLqpPgdxI15I3uy4/cGEsFpMIiJSN2gPQkRELKkgRETEkgpCREQsqSBERMSSCkJE\nRCypIERExJIKQkRELKkgRETEkgpCREQsqSBERMSSCkJERCypIERExJIKQkRELKkgRETEkgpCREQs\nuez7IM6cOcP48ePJz8+ntLSUUaNGERQUxJQpUwBo3bo1U6dOBeC9995j/fr12Gw2XnnlFR5//HEK\nCwtJSEigsLAQHx8fUlJSaNSokaviiojIJVxWEKtWraJFixYkJCRw4sQJhg8fTlBQEImJibRr146E\nhAS++eYbWrZsybp161i2bBmnT58mLi6OHj16sGTJEjp37sxvfvMb0tLSWLx4MWPHjnVVXBERuYTL\nDjE1btyYvLw8AAoKCmjUqBE///wz7dq1A6BXr16kp6eza9cuQkND8fb2JiAggDvuuINDhw6Rnp5O\nREREpXlFRKTmuGwP4oknnmDlypVERERQUFDAokWLeOONNxw/DwwMJDs7m0aNGhEQEOCYHhAQQHZ2\nNjk5OY7pgYGBZGVlOV1m48Y+eHp63HD2oCC/Gx7DleO5kjtlBffK605Zwb3yulNWcJ+8LiuIL774\ngmbNmvH+++9z4MABRo0ahZ/fv1eKMcbycVbTq5r3Urm5RdcX9hLZ2YXVMg6c/0WozvFcyZ2ygnvl\ndaes4F553Skr1M28VRWWyw4xZWRk0KNHDwDatGnDuXPnyM3Ndfz8xIkTBAcHExwcTE5OjuX07Ozs\nStNERKTmuKwgmjdvTmZmJgA///wzvr6+3HvvvezZsweAjRs3EhoaymOPPcbWrVspKSnhxIkTZGVl\ncd9999G9e3fWr19faV4REak5LjvE9PTTT5OYmMiwYcMoKytjypQpBAUF8Yc//IGKigrat29Pt27d\nABg8eDDDhg3DZrMxZcoU7HY78fHxjB07lri4OPz9/Zk9e7arooqIiAWbudoD/G7gRo7rvZC82XH7\ngwlh1REHqJvHG6viTlnBvfK6U1Zwr7zulBXqZt4aPwchIiLuTQUhIiKWVBAiImJJBSEiIpZUECIi\nYkkFISIillQQIiJiSQUhIiKWVBAiImJJBSEiIpZUECIiYkkFISIillQQIiJiSQUhIiKWVBAiImJJ\nBSEiIpZUECIiYkkFISIillQQIiJiSQUhIiKWVBAiImJJBSEiIpZUECIiYkkFISIiljxdOfiaNWt4\n77338PT05NVXX6V169aMGzeO8vJygoKCmD17Nt7e3qxZs4YlS5Zgt9sZPHgwgwYNorS0lAkTJnDs\n2DE8PDyYOXMmd911lyvjiojIRVy2B5Gbm0tqaiqffPIJ77zzDl9//TXz5s0jLi6OTz75hObNm7N8\n+XKKiopITU3lo48+YunSpSxZsoS8vDzWrl2Lv78/n376KSNGjCAlJcVVUUVExILLCiI9PZ2uXbvS\noEEDgoODefPNN9m1axfh4eEA9OrVi/T0dDIzMwkJCcHPz4969erRsWNHMjIySE9PJyIiAoBu3bqR\nkZHhqqgiImLBZYeYjh49SnFxMSNGjKCgoIDRo0dz9uxZvL29AQgMDCQ7O5ucnBwCAgIcjwsICLhs\nut1ux2azUVJS4ni8lcaNffD09Ljh7EFBfjc8hivHcyV3ygruldedsoJ75XWnrOA+eV16DiIvL48F\nCxZw7Ngxnn32WYwxjp9dfPti1zr9Yrm5RdcX9BLZ2YXVMg6c/0WozvFcyZ2ygnvldaes4F553Skr\n1M28VRWWyw4xBQYG8vDDD+Pp6cndd9+Nr68vvr6+FBcXA3DixAmCg4MJDg4mJyfH8bisrCzH9Ozs\nbABKS0sxxlxx70FERKqXywqiR48e7Ny5k4qKCnJzcykqKqJbt25s2LABgI0bNxIaGkr79u3Zt28f\nBQUFnDlzhoyMDDp16kT37t1Zv349AFu2bKFLly6uiioiIhZcdoipSZMmREZGMnjwYAAmTZpESEgI\n48ePJy0tjWbNmhETE4OXlxcJCQm8+OKL2Gw2Ro0ahZ+fH3379mXHjh0MHToUb29vkpOTXRVVREQs\n2MzVHNx3EzdyXO+F5M2O2x9MCKuOOEDdPN5YFXfKCu6V152ygnvldaesUDfz1vg5CBERcW8qCBER\nsaSCEBERSyoIERGxpIIQERFLKggREbGkghAREUsqCBERsaSCEBERSyoIERGxpIIQERFLKggREbGk\nghAREUsqCBERsaSCEBERSyoIERGxpIIQERFLKggREbGkghAREUsqCBERsaSCEBERSyoIERGxpIIQ\nERFLKggREbGkghAREUsuLYji4mJ69+7NypUr+eWXX4iPjycuLo7XXnuNkpISANasWcNTTz3FoEGD\n+PzzzwEoLS0lISGBoUOHMmzYMI4cOeLKmCIiYsGlBbFo0SIaNmwIwLx584iLi+OTTz6hefPmLF++\nnKKiIlJTU/noo49YunQpS5YsIS8vj7Vr1+Lv78+nn37KiBEjSElJcWVMERGx4LKCOHz4MIcOHaJn\nz54A7Nq1i/DwcAB69epFeno6mZmZhISE4OfnR7169ejYsSMZGRmkp6cTEREBQLdu3cjIyHBVTBER\nqYKnqwaeNWsWSUlJrF69GoCzZ8/i7e0NQGBgINnZ2eTk5BAQEOB4TEBAwGXT7XY7NpuNkpISx+Or\n0rixD56eHjecPSjI74bHcOV4ruROWcG98rpTVnCvvO6UFdwnr0sKYvXq1XTo0IG77rrL8ufGmGqZ\nfqnc3KKrC+hEdnZhtYwD538RqnM8V3KnrOBeed0pK7hXXnfKCnUzb1WF5ZKC2Lp1K0eOHGHr1q0c\nP34cb29vfHx8KC4upl69epw4cYLg4GCCg4PJyclxPC4rK4sOHToQHBxMdnY2bdq0obS0FGOM070H\nERGpXi45BzF37lxWrFjBZ599xqBBgxg5ciTdunVjw4YNAGzcuJHQ0FDat2/Pvn37KCgo4MyZM2Rk\nZNCpUye6d+/O+vXrAdiyZQtdunRxRUwREbkCl52DuNTo0aMZP348aWlpNGvWjJiYGLy8vEhISODF\nF1/EZrMxatQo/Pz86Nu3Lzt27GDo0KF4e3uTnJxcUzFFROT/5/KCGD16tOP2hx9+eNnPo6KiiIqK\nqjTNw8ODmTNnujqaiIhcgT5JLSIillQQIiJiSQUhIiKWVBAiImJJBSEiIpZUECIiYkkFISIillQQ\nIiJiSQUhIiKWVBAiImJJBSEiIpZUECIiYkkFISIillQQIiJiSQUhIiKWVBAiImJJBSEiIpZUECIi\nYkkFISIillQQIiJiSQUhIiKWVBAiImJJBSEiIpZUECIiYsnTlYO/9dZbfPfdd5SVlfHyyy8TEhLC\nuHHjKC8vJygoiNmzZ+Pt7c2aNWtYsmQJdrudwYMHM2jQIEpLS5kwYQLHjh3Dw8ODmTNnctddd7ky\nroiIXMRlBbFz507+8Y9/kJaWRm5uLgMGDKBr167ExcURHR3NnDlzWL58OTExMaSmprJ8+XK8vLwY\nOHAgERERbNmyBX9/f1JSUti+fTspKSnMnTvXVXFFROQSLjvE9Oijj/L2228D4O/vz9mzZ9m1axfh\n4eEA9OrVi/T0dDIzMwkJCcHPz4969erRsWNHMjIySE9PJyIiAoBu3bqRkZHhqqgiImLBZXsQHh4e\n+Pj4ALB8+XJ+/etfs337dry9vQEIDAwkOzubnJwcAgICHI8LCAi4bLrdbsdms1FSUuJ4vJXGjX3w\n9PS44exBQX43PIYrx3Mld8oK7pXXnbKCe+V1p6zgPnldeg4CYNOmTSxfvpwPPviAPn36OKYbYyzn\nv9bpF8vNLbq+kJfIzi6slnHg/C9CdY7nSu6UFdwrrztlBffK605ZoW7mraqwXPoupm3btvHOO++w\nePFi/Pz88PHxobi4GIATJ04QHBxMcHAwOTk5jsdkZWU5pmdnZwNQWlqKMeaKew8iIlK9XFYQhYWF\nvPXWW/zpT3+iUaNGwPlzCRs2bABg48aNhIaG0r59e/bt20dBQQFnzpwhIyODTp060b17d9avXw/A\nli1b6NKli6uiioiIBZcdYlq3bh25ubmMGTPGMS05OZlJkyaRlpZGs2bNiImJwcvLi4SEBF588UVs\nNhujRo3Cz8+Pvn37smPHDoYOHYq3tzfJycmuiioiIhZs5moO7ruJGzmu90LyZsftDyaEVUccoG4e\nb6yKO2UF98rrTlnBvfK6U1aom3lr5RyEiIi4LxWEiIhYUkGIiIglFYSIiFhSQYiIiCUVhIiIWFJB\niIiIJRWEiIhYcvnF+m5VF3/wDqr3w3ciIjVBexAiImJJBSEiIpZUECIiYkkFISIillQQIiJiSQUh\nIiKWVBAiImJJBSEiIpZUECIiYkkFISIillQQIiJiSQUhIiKWVBAiImJJBSEiIpZ0uW83cvElxHX5\ncBFxtTpdEDNmzCAzMxObzUZiYiLt2rWr7UgiIreMOlsQf/vb3/jpp59IS0vj8OHDJCYmkpaWVtux\nRERuGXW2INLT0+nduzcA9957L/n5+Zw+fZoGDRrUcrKbU3V/A56+UU/E/dmMMaa2Q1hJSkri8ccf\nd5REXFwc06dPp0WLFrWcTETk1uA272Kqoz0mInLTqrMFERwcTE5OjuN+VlYWQUFBtZhIROTWUmcL\nonv37mzYsAGAH374geDgYJ1/EBGpQXX2JHXHjh1p27YtQ4YMwWazMXny5NqOJCJyS6mzJ6lFRKR2\n1dlDTCIiUrtUECIiYkkFISIillQQIiJiSQUhIiKWVBAiImJJBSEiIpZUECIiYkkFISIillQQIiJi\nSQUhIiKWVBAiImJJBeHGWrduzfHjxytNW7lyJc899xwAf/7zn5k7d+4Vx8jMzOTAgQOuiuhS5eXl\nPPvss4SFhXHw4MEaW+6uXbuIiIioseVt2rSJHj16XHZF44ULFxIVFUVUVBTt27enR48ejvuHDx++\npmUMHz6cH374oTpjW/rss89cvowLoqKiKn2njFy7Onu5b7lxw4YNczrPihUreOSRR2jTpk0NJKpe\nWVlZ7N69m++//x4vL6/ajuMymzdvZuDAgYwZM6bS9JEjRzJy5EgA4uPjGThwIP3797+uZSxZsuSG\nczqTnZ3Ne++9x+DBg12+LID169fXyHJuZtqDuInNnz+f3//+9wB8+eWXPPnkk0RHR9OvXz927drF\np59+yhdffMHs2bP58MMPqaio4I9//KNjK3TChAkUFRUB57+0qU+fPvTp04cFCxY4xjh69Cg9evRg\nxowZjkL6+uuv6devH5GRkcTGxrJ//37g/Jb3008/zfTp0wkPDyc2NpbMzEzi4+Pp3r078+bNs3we\nBw4cYMiQIURFRdG/f3+2bdtGeXk58fHxVFRU0K9fv8v2gubPn88bb7zBqFGjCA8PZ+DAgWRlZQEQ\nFhbGnj17HPNeuH/huSxevJjIyEgiIyP5+9//zksvvURoaCgTJ06stIxZs2YRGRlJVFQUGRkZAJSU\nlDBt2jQiIyMJCwvjnXfeqbScBQsWEBkZybFjxyqNVdW6X7JkCRs2bGDZsmVMmjTpml7/CRMmMHPm\nTPr168eXX37J2bNnGTNmjCPbrFmzqlwH//3f/02/fv0IDQ1l3bp1luP/9a9/5YknniAyMpKXX36Z\nvLw8oOrXf8iQIRw7doyoqChKSko4dOgQw4YNIzIykn79+rFv3z7HunjzzTfp3r07Q4cO5d133yU+\nPh6AvLw8XnvtNSIjI+nbty/vvvuuI0/r1q3505/+RGRkJOXl5ZX2sNPS0oiKiiIsLIzXX3+d4uJi\nAP72t78xYMAA+vbtS3R0NF9++eU1reObnhG31apVK/PLL79UmrZixQozfPhwY4wx8+bNM4mJicYY\nY7p06WKOHj1qjDFm9+7dZsaMGcYYY4YNG2ZWr15tjDFm7dq1JiYmxpw5c8aUlZWZ3/3udyY1NdUY\nY8yAAQPMxx9/bIwx5sMPPzQPPfSQ2blzpzly5Ihp27atWblypTHGmNLSUtOpUyezd+9eY4wx8+fP\nd+TZuXOnadu2rdm5c6epqKgwTz31lImNjTVFRUXm4MGD5sEHHzTFxcWVnk95ebmJjo42f/nLX4wx\nxnz//ffm0UcfNYWFhebIkSPmgQcesFw38+bNM127djVHjx41FRUV5qWXXjILFy40xhjTq1cvs3v3\nbse8F+4fOXLEPPjgg2bVqlXGGGNGjx5tevbsaU6ePGlOnTplHnroIfPTTz+ZnTt3mgceeMCsXbvW\nGGNMWlqa6d+/vzHGmAULFpjhw4ebc+fOmTNnzpiYmBizefNmx3ImTZpkmfdK6378+PGO21W5+HW8\nYPz48aZfv36Odfr++++b3/zmN6aiosLk5eWZzp07O9bDpetg6dKlxhhj1q1bZyIiIi5b3pkzZ0zn\nzp3NwYMHjTHGTJs2zUyZMsXp69+7d29jzPnXtU+fPuazzz4zxhizZ88e06NHD1NaWmo2b95sevfu\nbU6fPm1yc3NNVFSUGTZsmDHGmKSkJJOUlGSMMSY3N9f07NnT8RxatWplFi1a5Mh44f/H7t27Tdeu\nXc3x48cdYyQnJxtjjImNjTW7du0yxhjzz3/+07z++utXXM+3Gu1BuLn4+HjHVmdUVBRz5syxnC8w\nMJBly5bx888/06lTp8u2hgG2bt1KTEwMPj4+eHh4EBsby7fffktxcTE//PADTz75JADPPPMM5qLv\nmSotLXUck/f09GTHjh106NBwmDHDAAAgAElEQVQBgE6dOnHkyBHHvP7+/nTp0gWbzcb9999P586d\nqV+/Pvfffz/l5eWcOnWqUqajR4+Sk5PDE088AUBISAjNmjVzbG1eSadOnbjjjjuw2Ww88MAD/PLL\nL04fU1ZWRlRUFACtWrUiJCSEgIAAGjduTFBQkGMv5LbbbiM6OhqA6Oho9u/fz7lz59iyZQtxcXF4\ne3vj4+ND//792bhxo2P8nj17Wi63qnV/o7p27cptt90GwAsvvMDChQux2Ww0bNiQ+++/n6NHj1qu\ng9jYWADatm172d4OQEZGBk2bNqVVq1YAjB07lokTJzp9/S/48ccfOXnyJAMHDgTgkUceISAggL17\n97Jnzx569uyJr68vjRo1crz2AN988w1xcXEANGrUiIiIiErryWr9bt68mb59+9KkSRMAhg4d6nhN\nAgMDWb16NYcPH+aee+4hJSXFyRq9tegchJtbunQpTZs2ddxfuXIla9asuWy+RYsWsWjRImJjY/nV\nr35FYmIinTt3rjTPqVOnaNiwoeN+w4YNOXnyJPn5+dhsNvz9/QHw8vIiMDDQMZ+Hh0el7wtfunQp\nq1atoqSkhJKSEmw2m+Nnvr6+jtt2ux0fHx8AbDYbdrud8vLyyzL5+flVGsPf359Tp05x1113XXHd\n+Pn5Vcp46dhWPDw8qFev3mX5Lh2jUaNG2O3nt68uPPf8/HwKCwuZOXOmo6hLSkpo166dY4yL1++l\nz9Nq3d+oi8f817/+RXJyMj/++CN2u53jx487iuBiHh4ejudtt9upqKi4bJ7c3FzH7wOAt7e34/aV\nXv8LCgoKKC4udpQswOnTp8nLy6OgoMDxxxyodPvUqVOVluvv7+8obTj/ulyqsLCQr776iu3btwNg\njKG0tBSAGTNmsGjRIp5//nnq1avH66+/7thAEBXELePuu+9m5syZVFRUsHr1ahISEti2bVuleW6/\n/XbHcWQ4f7z39ttvp0GDBhhjOHv2LPXr16esrOyyLf0LMjIyWLx4MZ9//jl33nkn3377LUlJSded\nOzAwkPz8fIwxjj80eXl5lQrqWl36Ry8/P/+ax7j4MQUFBcD5P07BwcG88MIL9OrV65rGq2rdV6c3\n3niDtm3bkpqaioeHB0OGDLnusRo3bkxubq7j/tmzZ8nPz+fYsWNX9foHBwfj6+treSI5IyPDce4L\nzp/cvuDCemrWrBlwdespODiYAQMGMH78+Mt+dvvtt5OUlERSUhLbt29n9OjRhIaGVtqQuZXpENMt\n4NSpUzz//POcPn0au91O+/btHX9sPT09KSwsBM7vnq9Zs4azZ89SVlbG8uXLefzxx/H19eXee+91\nnMBLS0uz3Cq8sKzAwECaNWvG2bNnWbVqFUVFRZUOSV2LO++8k6ZNmzpOlGZkZJCTk1Npq/xaBQUF\nOU5qr1u3jnPnzl3zGMXFxXz11VcAbNiwgZCQELy9vQkPD+fzzz+nvLwcYwwLFy7kr3/9q9Pxqlr3\n1enkyZM88MADeHh48O233/LTTz9V+kN8LR555BGys7P5/vvvgfNvuU1NTb3i6+/p6UlRURFlZWXc\ncccdNG3a1FEQp06d4vXXX6eoqIiQkBC2bt1KcXExBQUFlU4c9+zZk7S0NMdjvvrqqyoP210QFhbG\nxo0bHRs1mzZt4t1336W0tJT4+HjHHkjbtm3x9PR07BmK9iBuCQEBAYSGhvLUU0/h4eGBl5cX06dP\nB6B3797Mnj2bI0eOMGHCBA4ePEhsbCzGGLp06cKzzz4LwOTJk0lKSuL9998nJiaGJk2aWJZEaGgo\nn3zyCb1796ZJkyYkJiaSmZnJq6++elVvu72UzWZjzpw5TJ48mQULFlC/fn3efvttfHx8qtyLcWbk\nyJFMnjyZzz77jMjISO67775rHqNly5bs3buXlJQU7HY7ycnJAMTFxXH06FGeeOIJjDE89NBDDB8+\n3Ol4UVFRVa776vK73/2OmTNnsnDhQsLDw3nllVeYN28eDzzwwDWPVb9+febPn8/YsWMBaN68OcnJ\nyfj6+lb5+s+cOZOGDRvSvXt3Vq1axZw5c5gyZQpz587Fbrfz/PPP4+PjQ0REBFu3biUqKormzZsT\nHR1Neno6AGPGjGHKlClERUVht9t56aWXnG4stG3blhEjRjje9RYYGMjUqVPx8vJi4MCBjs8N2e12\nJk2aRP369a95fdysbOZ6N+3klnPxYZ7HHnuMjz76yC0/PyF138W/ax9//DE7duwgNTW1llPderQv\nJVfl1VdfZfHixQCkp6djjOGee+6p3VByU9q/fz/h4eHk5+dTVlbGxo0bHe+KkpqlPQi5KocPH2bi\nxInk5+fj5eXF2LFjq/0YucgF8+bN44svvsDDw4MOHTowdepUHfqpBSoIERGxpENMIiJi6aZ6F1N2\ndmFtR7hM48Y+5OZe31sJa5o7ZQX3yutOWcG98rpTVqibeYOC/Cynaw/CxTw9PWo7wlVzp6zgXnnd\nKSu4V153ygrulVcFISIillQQIiJiSQUhIiKWVBAiImJJBSEiIpZUECIiYkkFISIillQQIiJiSQUh\nIiKWVBAiImJJBSEiIpZUECIiYkkFISIillQQIiJiSQUhIiKWVBAiImJJBSEiIpZUECIiYkkFISIi\nllQQIiJiSQUhIiKWVBAiImLJ01UDnzlzhvHjx5Ofn09paSmjRo0iKCiIKVOmANC6dWumTp0KwHvv\nvcf69eux2Wy88sorPP744xQWFpKQkEBhYSE+Pj6kpKTQqFEjV8UVEZFLuKwgVq1aRYsWLUhISODE\niRMMHz6coKAgEhMTadeuHQkJCXzzzTe0bNmSdevWsWzZMk6fPk1cXBw9evRgyZIldO7cmd/85jek\npaWxePFixo4d66q4IiJyCZcdYmrcuDF5eXkAFBQU0KhRI37++WfatWsHQK9evUhPT2fXrl2Ehobi\n7e1NQEAAd9xxB4cOHSI9PZ2IiIhK84qISM255j2IiooK7HbnvfLEE0+wcuVKIiIiKCgoYNGiRbzx\nxhuOnwcGBpKdnU2jRo0ICAhwTA8ICCA7O5ucnBzH9MDAQLKyspwus3FjHzw9Pa71KblcUJBfbUe4\nau6UFdwrrztlBffK605ZwX3yOi2IlStXcvbsWZ5++mni4+M5fvw4v/3tb4mLi7vi47744guaNWvG\n+++/z4EDBxg1ahR+fv9eKcYYy8dZTa9q3kvl5hZd1Xw1KSjIj+zswtqOcVXcKSu4V153ygrulded\nskLdzFtVYTndFUhLS2PQoEFs2rSJ+++/n6+//povv/zS6QIzMjLo0aMHAG3atOHcuXPk5uY6fn7i\nxAmCg4MJDg4mJyfHcnp2dnalaSIiUnOcFsRtt92Gt7c333zzDdHR0Vd1eAmgefPmZGZmAvDzzz/j\n6+vLvffey549ewDYuHEjoaGhPPbYY2zdupWSkhJOnDhBVlYW9913H927d2f9+vWV5hURkZpzVecg\npk6dSkZGBtOmTWPv3r2UlJQ4fczTTz9NYmIiw4YNo6ysjClTphAUFMQf/vAHKioqaN++Pd26dQNg\n8ODBDBs2DJvNxpQpU7Db7cTHxzN27Fji4uLw9/dn9uzZN/ZMRUTkmtiMkwP8WVlZrFu3jl//+te0\nbNmStWvXct9999GmTZuaynjV6tpxPaibxxur4k5Zwb3yulNWcK+87pQV6mbe6z4HMWfOHJ577jla\ntmwJwJNPPqmteRGRW0CVh5jWrFnDsmXL+Mc//sEzzzzjmF5WVuY4eSwiIjevKgviP/7jP+jSpQv/\n+Z//yejRox3T7XY79913X42EExGR2nPFk9RNmjRh6dKlFBYWOj4VDVBYWKjrIomI3OScvotp2rRp\nrFixgoCAAMcH1mw2G19//bXLw4mISO1xWhC7du1i586d3HbbbTWRR0RE6gin72Jq3ry5ykFE5Bbk\ndA+iadOmPPPMMzzyyCN4ePz7QnivvfaaS4OJiEjtcloQjRo1omvXrjWRRURE6hCnBfHKK6+Qm5vL\n0aNHCQkJuerLfYuIiHtz+pf+f/7nf3j66aeZOHEiAG+++SbLly93eTAREaldTgvigw8+4IsvvqBx\n48YAjB8/nrS0NJcHExGR2uW0IPz8/Khfv77jfr169fDy8nJpKBERqX1Oz0E0btyYVatWce7cOX74\n4QfWrVtX6StCRUTk5uR0D2Lq1Kns27ePM2fOMGnSJM6dO8e0adNqIpuIiNQip3sQ/v7+/OEPf6iJ\nLCIiUoc4LYjVq1ezZMkSCgsLufi7hXQtJhGRm5vTgli4cCHTpk2jadOmNZFHRETqCKcF0bJlSzp3\n7lwTWWrVC8mbHbc/mBBWi0lEROoGpwUxZMgQXnjhBdq3b1/pWkyvvPKKS4OJiEjtcvouplmzZtGk\nSROMMZSVlTn+iYjIzc3pHkRQUBAzZ86siSwiIlKHOC2I0NBQVq5cycMPP4yn579nv+uuu1waTERE\napfTgvj0008vm6avHBURufk5LYjNmzc7m6VKa9as4b333sPT05NXX32V1q1bM27cOMrLywkKCmL2\n7Nl4e3uzZs0alixZgt1uZ/DgwQwaNIjS0lImTJjAsWPH8PDwYObMmdprERGpQU4LYty4cZbT33rr\nrSs+Ljc3l9TUVFasWEFRURHz589nw4YNxMXFER0dzZw5c1i+fDkxMTGkpqayfPlyvLy8GDhwIBER\nEWzZsgV/f39SUlLYvn07KSkpzJ079/qepYiIXDOn72Lq2rWr41+nTp0oLy/nV7/6ldOB09PT6dq1\nKw0aNCA4OJg333yTXbt2ER4eDkCvXr1IT08nMzOTkJAQ/Pz8qFevHh07diQjI4P09HQiIiIA6Nat\nGxkZGTf4VEVE5Fo43YMYMGBApfuDBw/m5Zdfdjrw0aNHKS4uZsSIERQUFDB69GjOnj2Lt7c3AIGB\ngWRnZ5OTk1Pp6rABAQGXTbfb7dhsNkpKShyPt9K4sQ+enh5V/vxqBQX53fAYrhzPldwpK7hXXnfK\nCu6V152ygvvkdVoQFRUVle7/8ssv/Otf/7qqwfPy8liwYAHHjh3j2WefrXQtp4tvX+xap18sN7fo\nqnI5k51dWC3jwPlfhOocz5XcKSu4V153ygruldedskLdzFtVYTktiAcffBCbzQac/yPt5+fHb3/7\nW6cLDAwMdLw19u6778bX1xcPDw+Ki4upV68eJ06cIDg4mODgYHJychyPy8rKokOHDgQHB5OdnU2b\nNm0oLS3FGHPFvQcREaleTs9BHDhwgP3797N//34OHDjA7t27eemll5wO3KNHD3bu3ElFRQW5ubkU\nFRXRrVs3NmzYAMDGjRsJDQ2lffv27Nu3j4KCAs6cOUNGRgadOnWie/furF+/HoAtW7bQpUuXG3yq\nIiJyLZzuQezZs4fPP/+cWbNmAfD8888zcuRIHn300Ss+rkmTJkRGRjJ48GAAJk2aREhIiOM7rZs1\na0ZMTAxeXl4kJCTw4osvYrPZGDVqFH5+fvTt25cdO3YwdOhQvL29SU5OroanKyIiV8tmnBzcHzp0\nKMnJyTRv3hw4f/J57Nixlh+gq203clzPVVdzrYvHG6viTlnBvfK6U1Zwr7zulBXqZt6qzkE4PcRk\njHGUA8Cdd96J3e70YSIi4uacHmJq1qwZs2fPpnPnzhhj2LZtm748SETkFuB0V2DmzJn4+vry6aef\nsmzZMpo0acK0adNqIpuIiNQip3sQt912G8899xw//vgjdrudFi1aUL9+/ZrIJiIitchpQWzatIkp\nU6bQtGlTKioqyMnJ4c033+Txxx+viXwiIlJLnBbEe++9x5o1axyXvThx4gSvvfaaCkJE5Cbn9ByE\nl5dXpWslNWnSBC8vL5eGEhGR2ud0D8LX15cPPviAbt26AbB9+3Z8fX1dHkxERGqX04KYPn06b7/9\nNmvWrMFms9G+fXtmzJhRE9lERKQWOS2IwMBA3njjjZrIIiIidUiVBREWFua4iqsVfSe1iMjNrcqC\n+OijjwBIS0sjKCiIxx57jPLycr799luKiqrnexdERKTuqrIg7r77bgD+93//lw8//NAxvW3btlf1\njXIiIuLenL7N9eTJk2zfvp2ioiKKi4tJT0/n2LFjNZFNRERqkdOT1FOnTmXWrFn83//9HwD33Xcf\nSUlJLg8mIiK1y2lBPPzwwyxbtqwmsoiISB2iL3YQERFLKggREbFUZUGsWLECgM8//7zGwoiISN1R\n5TmIRYsWUVpaypIlSyw/MDdw4ECXBhMRkdpVZUGMGzeOb775hsLCQr777rvLfq6CEBG5uVVZEH36\n9KFPnz5s2LCByMjImswkIiJ1gNO3uXbo0IHExET27duHzWajQ4cOjBkzptJ3RIiIyM3H6buYJk+e\nTNu2bZkzZw7/9V//RcuWLUlMTKyJbCIiUouc7kGcPXuWZ555xnG/VatWbN68+aoGLy4u5sknn2Tk\nyJF07dqVcePGUV5eTlBQELNnz8bb25s1a9awZMkS7HY7gwcPZtCgQZSWljJhwgSOHTuGh4cHM2fO\n5K677rr+ZykiItfM6R7E2bNnycrKctw/fvw4JSUlVzX4okWLaNiwIQDz5s0jLi6OTz75hObNm7N8\n+XKKiopITU3lo48+YunSpSxZsoS8vDzWrl2Lv78/n376KSNGjCAlJeU6n56IiFwvp3sQI0eOJDY2\nlqCgIIwxnDp1iunTpzsd+PDhwxw6dIiePXsCsGvXLqZOnQpAr169+OCDD2jRogUhISH4+fkB0LFj\nRzIyMkhPTycmJgaAbt266ZCWiEgtcFoQPXv2ZNOmTfzrX/8CoEWLFtx2221OB541axZJSUmsXr0a\nOL8n4u3tDZz/lrrs7GxycnIqnewOCAi4bLrdbsdms1FSUuJ4fFUaN/bB09PDaTZngoL8bngMV47n\nSu6UFdwrrztlBffK605ZwX3yOi0IgHr16tGmTZurHnT16tV06NChyvMGxphqmX6p3Nzq+SKj7OzC\nahkHzv8iVOd4ruROWcG98rpTVnCvvO6UFepm3qoK66oK4lpt3bqVI0eOsHXrVo4fP463tzc+Pj4U\nFxdTr149Tpw4QXBwMMHBweTk5Dgel5WVRYcOHQgODiY7O5s2bdpQWlqKMcbp3oOIiFQvpyepr3br\n/WJz585lxYoVfPbZZwwaNIiRI0fSrVs3NmzYAMDGjRsJDQ2lffv27Nu3j4KCAs6cOUNGRgadOnWi\ne/furF+/HoAtW7bQpUuXa84gIiI3xmlBPPvss9WyoNGjR7N69Wri4uLIy8sjJiaGevXqkZCQwIsv\nvsjzzz/PqFGj8PPzo2/fvlRUVDB06FA+/vhjEhISqiWDiIhcPZtxsoswY8YMfH19efjhh/Hy8nJM\n79q1q8vDXasbOa73QvK/P9vxwYSw6ogD1M3jjVVxp6zgXnndKSu4V153ygp1M+91n4PYv38/AHv2\n7HFMs9lsdbIgRESk+jgtiKVLlwLnz0VYXfZbRERuTk7PQRw4cIDY2Fiio6MBSE1NJTMz0+XBRESk\ndjktiDfeeIMZM2YQFBQEQN++fZk5c6bLg4mISO1yWhCenp6VPiTXokULPD1d8vEJERGpQ66qII4c\nOeI4//DNN99c12cjRETEvTjdFRg/fjwjR47kn//8Jx07duTOO+9k1qxZNZFNRERqkdOCaN26NX/5\ny184deoU3t7eNGjQoCZyiYhILXNaEIcOHWL+/PkcOnQIm81Gq1ateOWVV2jZsmVN5BMRkVritCDG\njRtHXFwcr776KgDfffcdY8eOZcWKFS4PJyIitcdpQfj6+jJw4EDH/Xvvvddx0T0REbl5VfkupoqK\nCioqKujatSsbN27k9OnTnDlzhk2bNvHoo4/WZEYREakFVe5BPPjgg9hsNsu3tHp6ejJixAiXBhMR\nkdpVZUEcOHCgJnOIiEgd4/QcxIkTJ9iwYQOFhYWV9iZeeeUVlwYTEZHa5fST1L/97W/Zv38/paWl\nlJWVOf6JiMjNzekeRKNGjXRxPhGRW5DTgoiIiGDNmjU8/PDDeHh4OKY3a9bMpcFERKR2OS2IgwcP\n8pe//IVGjRo5ptlsNrZu3erKXCIiUsucFkRmZia7d+/G29u7JvKIiEgd4fQk9UMPPcS5c+dqIouI\niNQhV/U217CwMO69995K5yA+/vhjlwYTEZHa5bQg9IlpEZFbk9OCKC8vr4kcIiJSxzgtiIULFzpu\nl5aWcujQITp27EjXrl1dGkxERGqX04JYunRppfsnT54kJSXlqgZ/6623+O677ygrK+Pll18mJCSE\ncePGUV5eTlBQELNnz8bb25s1a9awZMkS7HY7gwcPZtCgQZSWljJhwgSOHTuGh4cHM2fO5K677rq+\nZykiItfMaUFcKjAwkB9//NHpfDt37uQf//gHaWlp5ObmMmDAALp27UpcXBzR0dHMmTOH5cuXExMT\nQ2pqKsuXL8fLy4uBAwcSERHBli1b8Pf3JyUlhe3bt5OSksLcuXOv60mKiMi1c1oQY8eOxWazOe7/\n8ssv2O1O3x3Lo48+Srt27QDw9/fn7Nmz7Nq1i6lTpwLQq1cvPvjgA1q0aEFISAh+fn4AdOzYkYyM\nDNLT04mJiQGgW7duJCYmXvuzExGR6+a0ILp16+a4bbPZaNCgAd27d3c6sIeHBz4+PgAsX76cX//6\n12zfvt3xgbvAwECys7PJyckhICDA8biAgIDLptvtdmw2GyUlJVf8wF7jxj54enpU+fOrFRTkd8Nj\nuHI8V3KnrOBeed0pK7hXXnfKCu6T12lBDBgw4IYWsGnTJpYvX84HH3xAnz59HNOtvojoeqZfLDe3\n6PpCXiI7u7BaxoHzvwjVOZ4ruVNWcK+87pQV3CuvO2WFupm3qsKqsiDCwsIqHVoyxji24nNycti/\nf7/ThW7bto133nmH9957Dz8/P3x8fCguLqZevXqcOHGC4OBggoODycnJcTwmKyuLDh06EBwcTHZ2\nNm3atKG0tBRjjC73ISJSg6osiM2bN182bdOmTaSkpPDUU085HbiwsJC33nqLjz76yHGhv27durFh\nwwb69+/Pxo0bCQ0NpX379kyaNImCggI8PDzIyMggMTGR06dPs379ekJDQ9myZQtdunS5gacpIiLX\n6qrexfSvf/2LadOm4eXlxbvvvntVbzddt24dubm5jBkzxjEtOTmZSZMmkZaWRrNmzYiJicHLy4uE\nhARefPFFbDYbo0aNws/Pj759+7Jjxw6GDh2Kt7c3ycnJ1/8sRUTkmtnMFQ7uFxUVkZqayjfffMPY\nsWN5/PHHazLbNbuR43ovJP97j+mDCWHVEQeom8cbq+JOWcG98rpTVnCvvO6UFepm3qrOQVT5ftW1\na9cSGxtLw4YNWbVqVZ0vBxERqV5VHmL6z//8T+655x62bdvG9u3bHdMvnKz+7//+7xoJKCIitaPK\ngvj6669rMoeIiNQxVRbEHXfcUZM5RESkjnF+zQwREbklqSBERMSSCkJERCypIERExJIKQkRELKkg\nRETEkgpCREQsqSBERMSSCkJERCxd1eW+5dpdfHVYqN4rxIqI1ATtQYiIiCUVhIiIWFJBiIiIJRWE\niIhYUkGIiIglFYSIiFhSQYiIiCUVhIiIWFJBiIiIJRWEiIhYqtOX2pgxYwaZmZnYbDYSExNp165d\nbUeqVRdfvkOX7hARV6uzBfG3v/2Nn376ibS0NA4fPkxiYiJpaWm1HUtE5JZRZwsiPT2d3r17A3Dv\nvfeSn5/P6dOnadCgQS0nuzlV98UFdbFCEfdnM8aY2g5hJSkpiccff9xREnFxcUyfPp0WLVrUcjIR\nkVuD25ykrqM9JiJy06qzBREcHExOTo7jflZWFkFBQbWYSETk1lJnC6J79+5s2LABgB9++IHg4GCd\nfxARqUF19iR1x44dadu2LUOGDMFmszF58uTajiQickupsyepRUSkdtXZQ0wiIlK7VBAiImJJBSEi\nIpZUECIiYkkFISIillQQIiJiSQUhIiKWVBAiImJJBSEiIpZUECIiYkkFISIillQQIiJiSQVxE2jd\nujXHjx+vNG3lypU899xzAPz5z39m7ty5VxwjMzOTAwcOuCqiS5WXl/Pss88SFhbGwYMHa2y5u3bt\nIiIiosaWt2nTJnr06HHZlY0XLlxIVFQUUVFRtG/fnh49ejjuHz58+LqWtXbtWs6cOVMdscnKymLL\nli3VMpYze/fu5be//W2NLOtWUGcv9y3VZ9iwYU7nWbFiBY888ght2rSpgUTVKysri927d/P999/j\n5eVV23FcZvPmzQwcOJAxY8ZUmj5y5EhGjhwJQHx8PAMHDqR///43tKy3336bLl264Ovre0PjwPnv\nl//uu+/o1avXDY/lzMMPP8zixYtdvpxbhfYgbgHz58/n97//PQBffvklTz75JNHR0fTr149du3bx\n6aef8sUXXzB79mw+/PBDKioq+OMf/+jYCp0wYQJFRUXA+S9v6tOnD3369GHBggWOMY4ePUqPHj2Y\nMWOGo5C+/vpr+vXrR2RkJLGxsezfvx84v+X99NNPM336dMLDw4mNjSUzM5P4+Hi6d+/OvHnzLJ/H\ngQMHGDJkCFFRUfTv359t27ZRXl5OfHw8FRUV9OvX77K9oPnz5/PGG28watQowsPDGThwIFlZWQCE\nhYWxZ88ex7wX7l94LosXLyYyMpLIyEj+/ve/89JLLxEaGsrEiRMrLWPWrFlERkYSFRVFRkYGACUl\nJUybNo3IyEjCwsJ45513Ki1nwYIFREZGcuzYsUpjVbXulyxZwoYNG1i2bBmTJk26ptc/Pz+fhIQE\nIiMjCQ8PZ/Xq1Y6fpaSkOJ7jc889R1ZWFuPGjeP//b//xzPPPMPevXsvG++dd94hPDycyMhIZs2a\nVWldX1jGiBEjOH36NN9//z0zZszgyy+/JCEhAYCNGzfSr18/wsPD+c1vfkNeXh4Ap06dIj4+nl69\nejFmzBgmTpzIwoULAauqXs8AACAASURBVNi/f7/jtY+JiWHHjh0A7Nixg7i4OEaPHs24cePYsWMH\nUVFRAP9fe/ceH9O9qH/8M7mMSCRImnDSqqoWbUXIcamQukaKvVU52NJEe2qfc6jS9jhFg6IoqmzX\n0qI4URVN3bZju7VuJdFLbJu+6EYvOy4lIZJI5P79/eHX2SIr6GU6mXrefyVr1vrOM2sm88z6rskM\nhYWFvPbaa4774J133nFkXblyJd27d+fxxx+nX79+P/lI6zfPiNtr1KiROXfuXLllH374oXn66aeN\nMcbMmzfPJCQkGGOMadOmjTl9+rQxxpjPPvvMvP7668YYY+Li4syGDRuMMcZs3rzZ9O7d2+Tl5ZmS\nkhIzdOhQs3DhQmOMMU8++aR57733jDHGLF++3DRt2tSkpqaa9PR088gjj5h169YZY4wpLi42LVu2\nNIcOHTLGGDN//nxHntTUVPPII4+Y1NRUU1ZWZvr27Wv69Olj8vPzzVdffWUefvhhU1BQUO72lJaW\nmu7du5s///nPxhhj/va3v5lWrVqZ3Nxck56ebh566CHLfTNv3jzTtm1bc/r0aVNWVmb+8z//07z1\n1lvGGGM6depkPvvsM8e6P/yenp5uHn74YbN+/XpjjDHDhw83HTt2NBcvXjSXLl0yTZs2Nd99951J\nTU01Dz30kNm8ebMxxpikpCTzxBNPGGOMWbBggXn66adNYWGhycvLM7179zYff/yx43rGjRtnmfdm\n+3706NGOnytz/f34g1GjRplXXnnFlJaWmoyMDNO+fXtz8uRJc+zYMdO9e3dTXFzsuD83btxoiouL\nTaNGjcyFCxcqjJ+ammpiYmLMlStXTGFhoXnyySfN9u3bzV//+lfTrl07k5uba0pKSkxcXJx5++23\njTHGzJ4924wfP94YY8w333xjWrRoYU6ePOnYTy+++KIxxpgpU6aY//7v/zbGGPPXv/7VNGvWzCxc\nuNCUlpaabt26mS1bthhjjDl06JBp3bq1ycvLM/v37zdhYWHm008/NcYYs3//fhMTE2OMMWbu3Lnm\n2WefNYWFhebKlSumV69eZs+ePSY7O9u0atXKXLlyxbHPly1bdtP9eqfSEcRvRHx8vONV5+OPP87s\n2bMt1wsKCmLNmjWcOXOGli1bVng1DLB792569+6Nr68vnp6e9OnTh/3791NQUMCXX37J7373OwCe\neuopzHXfN1VcXOyYk/fy8uLAgQM0b94cgJYtW5Kenu5YNyAggDZt2mCz2XjwwQdp3bo11atX58EH\nH6S0tJRLly6Vy3T69GkyMzPp2bMnAGFhYYSGhnLkyJFb7puWLVty9913Y7PZeOihhzh37twttykp\nKXG8Em3UqBFhYWEEBgZSu3ZtgoODHUch1apVo3v37gB0796dY8eOUVhYyK5du4iNjcVut+Pr68sT\nTzzB9u3bHeN37NjR8nor2/c/x65duxg0aBAeHh7cddddREdHs2PHDgICAsjIyGDz5s3k5OTwzDPP\n0KtXr5uOtWfPHjp16oSfnx92u5333nuPzp07Ex4ezscff0yNGjXw9PQkIiKi3P39g7179xIZGUnD\nhg0BGDhwIDt37sQYwxdffOF4bIWHh9O0aVMAvv32W7Kzsx37uXnz5oSEhPDll18C4OfnR6tWrSxv\n9w/3gZ+fH7169WLHjh34+PhgjOHDDz90PKaeffbZn76Df8N0DuI3IjExkbp16zp+X7duHZs2baqw\n3qJFi1i0aBF9+vThX/7lX0hISKB169bl1rl06RI1a9Z0/F6zZk0uXrxIdnY2NpuNgIAAALy9vQkK\nCnKs5+npWe57wxMTE1m/fj1FRUUUFRVhs9kcl10/t+3h4YGvry8ANpsNDw8PSktLK2Ty9/cvN0ZA\nQACXLl2iXr16N903/v7+5TLeOLYVT09PfHx8KuS7cYxatWrh4XHtddYPtz07O5vc3FymTZvmKOqi\noiKaNWvmGOP6/Xvj7bTa9z9Hbm4uw4cPx9PTE7g29dKzZ09CQ0OZN28ey5YtY9KkSbRp04ZJkyaV\nu09vlJWVVW5/V69eHYD8/Hxef/11PvvsMwAuX75M165dK2yfk5NDamqqo3zh2mMhOzub7Ozscre9\nTp06QMV9Atfu+4sXLxIQEFDpvszJyWHKlCnMnDkTuHYftGjRArvdzvLly3nnnXeYO3cuDz30EBMm\nTODBBx+sfCfeoVQQd5h7772XadOmUVZWxoYNGxg5ciT79u0rt85dd93lmBeGa3/sd911FzVq1MAY\nw9WrV6levTolJSUVXun/IC0tjSVLlvDBBx9wzz33sH//fsaPH/+TcwcFBZGdnY0xxlESly9fvumT\n2a14eHhQVlbm+D07O/tHj3H9Njk5OcC10ggJCeHZZ5/90SdmK9v3P0dwcDCLFy92vGq/Xtu2bWnb\nti15eXlMnz6d2bNnM3Xq1ErHql27NllZWY7fs7KysNlsrFq1ijNnzrB+/Xp8fX2ZOXNmudvxgzp1\n6hAVFcWf/vSnCpfVqFHDca4LICMjgwceeKDCPoF/7peioqJKs4aEhDB06FAee+yxCpc1bdqUefPm\nUVRUxNtvv82kSZNYtWpVpWPdqTTFdAe5dOkS//7v/86VK1fw8PAgPDzc8WTr5eVFbm4ucG36Y9Om\nTVy9epWSkhKSk5Pp0KEDfn5+NGzYkL/85S8AJCUllXtFf+N1BQUFERoaytWrV1m/fj35+fnlpqR+\njHvuuYe6deuyZcsW4FoBZWZmlntV/mMFBwc7Tmpv2bKFwsLCHz1GQUEBO3bsAGDbtm2EhYVht9vp\n0qULH3zwAaWlpRhjeOutt9i7d+8tx6ts3/8cXbp0Yc2aNcC1acApU6Zw7Ngx9uzZw5QpUygrK8PP\nz4/GjRs7juA8PDwchXe9zp0789FHH5Gbm0txcTFDhw7lwIEDXLp0iYYNG+Lr60t6ejp79+51PNl7\ne3s7HltRUVF8+umnnD59Grj2ttRp06YB0KxZM7Zu3QrA0aNHOXr0KHDtRU1gYKDjss8//5zs7GzH\nFNTNbvf198GCBQv45JNPOHbsGC+99BLFxcXY7XaaNm1a6eP4TqcjiDtIYGAgUVFR9O3bF09PT7y9\nvR2vFrt27crMmTNJT09nzJgxfPXVV/Tp0wdjDG3atGHQoEEATJgwgfHjx7Ns2TJ69+5NnTp1LP+4\noqKiWL16NV27dqVOnTokJCRw+PBhRowYcVtvu72RzWZj9uzZTJgwgQULFlC9enXmzp2Lr69vpUcx\nt/Lcc88xYcIE1q5dS0xMDA888MCPHuP+++/n0KFDzJo1Cw8PD6ZPnw5AbGwsp0+fpmfPnhhjaNq0\nKU8//fQtx3v88ccr3fc/1UsvvcSkSZOIiYkBoEOHDjRq1IgGDRqwZcsWYmJi8Pb25q677mLatGl4\neHgQExNDv379mD59Ot26dXOM1bJlSwYNGkSvXr2w2+107NiR7t278+CDD/LCCy/w+OOP07hxYxIS\nEhgxYgSJiYm0b9+elStXMmDAAJKSkpg0aRJDhw6lpKSEGjVqON5h99xzz/Hiiy/SrVs3IiIi6Ny5\ns6Ow/vSnPzFx4kTmzJmDn58fc+bMcUwBVmbQoEHMmDHDcR80a9aMwYMHU61aNerUqUOPHj0c5ycm\nTpz4s/bxb5XN/NSXdHLHun6a59FHH2XFihVu+f8TUvVc/9gaNmwYkZGRPPXUUy5OdefSFJP8KCNG\njHD8I1JKSgrGGO677z7XhpLfhBUrVjBs2DDKysrIyMjg888/Jzw83NWx7mg6gpAf5dSpU7zyyitk\nZ2fj7e3Nyy+//LPnyEUArly5wpgxYzh+/Dienp7079+fwYMHuzrWHU0FISIiljTFJCIiln5T72LK\nyMh1dYQKatf2JSsr/9YrVgHulBXcK687ZQX3yutOWaFq5g0O9rdcriMIJ/Py8nR1hNvmTlnBvfK6\nU1Zwr7zulBXcK68KQkRELKkgRETEkgpCREQsqSBERMSSCkJERCypIERExJIKQkRELKkgRETEkgpC\nREQsqSBERMSSCkJERCypIERExJIKQkRELKkgRETEkgpCREQsqSBERMSSCkJERCypIERExJIKQkRE\nLKkgRETEkgpCREQsqSBERMSSl7MGzsvLY/To0WRnZ1NcXMywYcMIDg5m4sSJADRu3JhJkyYBsHTp\nUrZu3YrNZuP555+nQ4cO5ObmMnLkSHJzc/H19WXWrFnUqlXLWXFFROQGTiuI9evX06BBA0aOHMn5\n8+d5+umnCQ4OJiEhgWbNmjFy5Ej27NnD/fffz5YtW1izZg1XrlwhNjaW9u3bs3LlSlq3bs0f//hH\nkpKSWLJkCS+//LKz4oqIyA2cNsVUu3ZtLl++DEBOTg61atXizJkzNGvWDIBOnTqRkpLCwYMHiYqK\nwm63ExgYyN13383JkydJSUkhOjq63LoiIvLrcdoRRM+ePVm3bh3R0dHk5OSwaNEiXnvtNcflQUFB\nZGRkUKtWLQIDAx3LAwMDycjIIDMz07E8KCiICxcu3PI6a9f2xcvL85e/MT9TcLC/qyPcNnfKCu6V\n152ygnvldaes4D55nVYQGzduJDQ0lGXLlnH8+HGGDRuGv/8/d4oxxnI7q+WVrXujrKz8nxbWiYKD\n/cnIyHV1jNviTlnBvfK6U1Zwr7zulBWqZt7KCstpU0xpaWm0b98egCZNmlBYWEhWVpbj8vPnzxMS\nEkJISAiZmZmWyzMyMsotExGRX4/TCqJ+/focPnwYgDNnzuDn50fDhg35/PPPAdi+fTtRUVE8+uij\n7N69m6KiIs6fP8+FCxd44IEHaNeuHVu3bi23roiI/HqcNsU0YMAAEhISiIuLo6SkhIkTJxIcHMyr\nr75KWVkZ4eHhREZGAtC/f3/i4uKw2WxMnDgRDw8P4uPjefnll4mNjSUgIICZM2c6K6qIiFiwmdud\n4HcDVW1eD6rmfGNl3CkruFded8oK7pXXnbJC1cz7q5+DEBER96aCEBERSyoIERGxpIIQERFLKggR\nEbGkghAREUsqCBERsaSCEBERSyoIERGxpIIQERFLKggREbGkghAREUsqCBERsaSCEBERSyoIERGx\npIIQERFLKggREbGkghAREUsqCBERsaSCEBERSyoIERGxpIIQERFLKggREbGkghAREUtezhx806ZN\nLF26FC8vL0aMGEHjxo0ZNWoUpaWlBAcHM3PmTOx2O5s2bWLlypV4eHjQv39/+vXrR3FxMWPGjOHs\n2bN4enoybdo06tWr58y4IiJyHacdQWRlZbFw4UJWr17N4sWL+eijj5g3bx6xsbGsXr2a+vXrk5yc\nTH5+PgsXLmTFihUkJiaycuVKLl++zObNmwkICOD9999nyJAhzJo1y1lRRUTEgtMKIiUlhbZt21Kj\nRg1CQkKYPHkyBw8epEuXLgB06tSJlJQUDh8+TFhYGP7+/vj4+BAREUFaWhopKSlER0cDEBkZSVpa\nmrOiioiIBadNMZ0+fZqCggKGDBlCTk4Ow4cP5+rVq9jtdgCCgoLIyMggMzOTwMBAx3aBgYEVlnt4\neGCz2SgqKnJsb6V2bV+8vDyddZN+suBgf1dHuG3ulBXcK687ZQX3yutOWcF98jr1HMTly5dZsGAB\nZ8+eZdCgQRhjHJdd//P1fuzy62Vl5f+0oE4UHOxPRkauq2PcFnfKCu6V152ygnvldaesUDXzVlZY\nTptiCgoKokWLFnh5eXHvvffi5+eHn58fBQUFAJw/f56QkBBCQkLIzMx0bHfhwgXH8oyMDACKi4sx\nxtz06EFERH5ZTiuI9u3bk5qaSllZGVlZWeTn5xMZGcm2bdsA2L59O1FRUYSHh3PkyBFycnLIy8sj\nLS2Nli1b0q5dO7Zu3QrArl27aNOmjbOiioiIBadNMdWpU4eYmBj69+8PwLhx4wgLC2P06NEkJSUR\nGhpK79698fb2ZuTIkQwePBibzcawYcPw9/enR48eHDhwgIEDB2K325k+fbqzooqIiAWbuZ3JfTdR\n1eb1oGrON1bGnbKCe+V1p6zgXnndKStUzby/+jkIERFxbyoIERGxpIIQERFLKggREbGkghAREUsq\nCBERsaSCEBERSyoIERGxpIIQERFLKggREbGkghAREUsqCBERsaSCEBERSyoIERGxpIIQERFLKggR\nEbGkghAREUsqCBERsaSCEBERSyoIERGxpIIQERFLKggREbGkghAREUsqCBERseTUgigoKKBr166s\nW7eOc+fOER8fT2xsLC+88AJFRUUAbNq0ib59+9KvXz8++OADAIqLixk5ciQDBw4kLi6O9PR0Z8YU\nERELTi2IRYsWUbNmTQDmzZtHbGwsq1evpn79+iQnJ5Ofn8/ChQtZsWIFiYmJrFy5ksuXL7N582YC\nAgJ4//33GTJkCLNmzXJmTBERseC0gjh16hQnT56kY8eOABw8eJAuXboA0KlTJ1JSUjh8+DBhYWH4\n+/vj4+NDREQEaWlppKSkEB0dDUBkZCRpaWnOiikiIpXwctbAM2bMYPz48WzYsAGAq1evYrfbAQgK\nCiIjI4PMzEwCAwMd2wQGBlZY7uHhgc1mo6ioyLF9ZWrX9sXLy9NJt+inCw72d3WE2+ZOWcG98rpT\nVnCvvO6UFdwnr1MKYsOGDTRv3px69epZXm6M+UWW3ygrK//2Av6KgoP9ycjIdXWM2+JOWcG98rpT\nVnCvvO6UFapm3soKyykFsXv3btLT09m9ezfff/89drsdX19fCgoK8PHx4fz584SEhBASEkJmZqZj\nuwsXLtC8eXNCQkLIyMigSZMmFBcXY4y55dGDiIj8spxyDmLOnDl8+OGHrF27ln79+vHcc88RGRnJ\ntm3bANi+fTtRUVGEh4dz5MgRcnJyyMvLIy0tjZYtW9KuXTu2bt0KwK5du2jTpo0zYoqIyE047RzE\njYYPH87o0aNJSkoiNDSU3r174+3tzciRIxk8eDA2m41hw4bh7+9Pjx49OHDgAAMHDsRutzN9+vRf\nK6aIiPx/NnO7E/xuoKrN60HVnG+sjDtlBffK605Zwb3yulNWqJp5KzsHof+kFhERSyoIERGxpIIQ\nERFLKggREbGkghAREUsqCBERsaSCEBERSyoIERGxpIIQERFLKggREbGkghAREUsqCBERsaSCEBER\nSyoIERGxpIIQERFLKggREbGkghAREUsqCBERsfSrfSd1Vffs9I8dP787prMLk4iIVA06ghAREUsq\nCBERsaSCEBERSyoIERGxpIIQERFLKggREbHk1Le5vvHGG3zxxReUlJTwX//1X4SFhTFq1ChKS0sJ\nDg5m5syZ2O12Nm3axMqVK/Hw8KB///7069eP4uJixowZw9mzZ/H09GTatGnUq1fPmXFFROQ6TiuI\n1NRUTpw4QVJSEllZWTz55JO0bduW2NhYunfvzuzZs0lOTqZ3794sXLiQ5ORkvL29+bd/+zeio6PZ\ntWsXAQEBzJo1i08++YRZs2YxZ84cZ8UVEZEbOG2KqVWrVsydOxeAgIAArl69ysGDB+nSpQsAnTp1\nIiUlhcOHDxMWFoa/vz8+Pj5ERESQlpZGSkoK0dHRAERGRpKWluasqCIiYsFpRxCenp74+voCkJyc\nzGOPPcYnn3yC3W4HICgoiIyMDDIzMwkMDHRsFxgYWGG5h4cHNpuNoqIix/ZWatf2xcvL82dnDw72\n/9ljOHM8Z3KnrOBeed0pK7hXXnfKCu6T1+kftbFz506Sk5N599136datm2O5McZy/R+7/HpZWfk/\nLeQNMjJyf5Fx4NoD4Zccz5ncKSu4V153ygruldedskLVzFtZYTn1XUz79u1j8eLFLFmyBH9/f3x9\nfSkoKADg/PnzhISEEBISQmZmpmObCxcuOJZnZGQAUFxcjDHmpkcPIiLyy3JaQeTm5vLGG2/w9ttv\nU6tWLeDauYRt27YBsH37dqKioggPD+fIkSPk5OSQl5dHWloaLVu2pF27dmzduhWAXbt20aZNG2dF\nFRERC06bYtqyZQtZWVm8+OKLjmXTp09n3LhxJCUlERoaSu/evfH29mbkyJEMHjwYm83GsGHD8Pf3\np0ePHhw4cICBAwdit9uZPn26s6KKiIgFm7mdyX038XPm9Zz1cd9Vcb6xMu6UFdwrrztlBffK605Z\noWrmdck5CBERcV8qCBERsaSCEBERSyoIERGxpIIQERFLKggREbGkghAREUsqCBERsaSCEBERSyoI\nERGxpIIQERFLKggREbGkghAREUsqCBERsaSCEBERSyoIERGxpIIQERFLKggREbGkghAREUsqCBER\nsaSCEBERSyoIERGx5OXqAL9Fz07/2PHzn2c94cIkIiI/nY4gRETEUpU+gnj99dc5fPgwNpuNhIQE\nmjVr5upIIiJ3jCpbEJ9++infffcdSUlJnDp1ioSEBJKSklwdS0TkjlFlCyIlJYWuXbsC0LBhQ7Kz\ns7ly5Qo1atRwcTLXuf7cxrtjOlf5cUXEvdmMMcbVIayMHz+eDh06OEoiNjaWqVOn0qBBAxcnExG5\nM7jNSeoq2mMiIr9ZVbYgQkJCyMzMdPx+4cIFgoODXZhIROTOUmULol27dmzbtg2AL7/8kpCQkDv6\n/IOIyK+typ6kjoiI4JFHHuEPf/gDNpuNCRMmuDqSiMgdpcqepBYREdeqslNMIiLiWioIERGxpIJw\nkjfeeIMBAwbQt29ftm/f7uo4t6WgoICuXbuybt06V0e5qU2bNtGrVy/69OnD7t27XR3npvLy8nj+\n+eeJj4/nD3/4A/v27XN1JEt///vf6dq1K6tWrQLg3LlzxMfHExsbywsvvEBRUZGLE/6TVdZnnnmG\nuLg4nnnmGTIyMlycsLwb8/5g3759NG7c2EWpbo8KwglSU1M5ceIESUlJLF26lNdff93VkW7LokWL\nqFmzpqtj3FRWVhYLFy5k9erVLF68mI8++sjVkW5q/fr1NGjQgMTERObOncvUqVNdHamC/Px8Jk+e\nTNu2bR3L5s2bR2xsLKtXr6Z+/fokJye7MOE/WWWdM2cO/fv3Z9WqVURHR7N8+XIXJizPKi9AYWEh\n77zzTpV/674KwglatWrF3LlzAQgICODq1auUlpa6ONXNnTp1ipMnT9KxY0dXR7mplJQU2rZtS40a\nNQgJCWHy5MmujnRTtWvX5vLlywDk5ORQu3ZtFyeqyG63s2TJEkJCQhzLDh48SJcuXQDo1KkTKSkp\nropXjlXWCRMmEBMTA5Tf31WBVV6AxYsXExsbi91ud1Gy26OCcAJPT098fX0BSE5O5rHHHsPT09PF\nqW5uxowZjBkzxtUxbun06dMUFBQwZMgQYmNjq8wTV2V69uzJ2bNniY6OJi4ujtGjR7s6UgVeXl74\n+PiUW3b16lXHk1dQUFCVmbaxyurr64unpyelpaWsXr2a3//+9y5KV5FV3m+++Ybjx4/TvXt3F6W6\nfVX2/yB+C3bu3ElycjLvvvuuq6Pc1IYNG2jevDn16tVzdZTbcvnyZRYsWMDZs2cZNGgQu3btwmaz\nuTqWpY0bNxIaGsqyZcs4fvw4CQkJVf4cz43c4Z3wpaWljBo1ikcffbTCdE5VM23aNMaNG+fqGLdF\nBeEk+/btY/HixSxduhR/f39Xx7mp3bt3k56ezu7du/n++++x2+3UrVuXyMhIV0erICgoiBYtWuDl\n5cW9996Ln58fly5dIigoyNXRLKWlpdG+fXsAmjRpwoULFygtLa3yR5S+vr4UFBTg4+PD+fPnK0yR\nVDWvvPIK9evX5/nnn3d1lJs6f/48X3/9Nf/zP/8DXPsIobi4uAonsKsKFYQT5Obm8sYbb7BixQpq\n1arl6ji3NGfOHMfP8+fP5+67766S5QDQvn17xowZw3/8x3+QnZ1Nfn5+lZzX/0H9+vU5fPgwMTEx\nnDlzBj8/vypfDgCRkZFs27aNJ554gu3btxMVFeXqSJXatGkT3t7ejBgxwtVRbqlOnTrs3LnT8Xvn\nzp2rbDmACsIptmzZQlZWFi+++KJj2YwZMwgNDXVhqt+GOnXqEBMTQ//+/QEYN24cHh5V91TagAED\nSEhIIC4ujpKSEiZOnOjqSBUcPXqUGTNmcObMGby8vNi2bRtvvvkmY8aMISkpidDQUHr37u3qmIB1\n1osXL1KtWjXi4+OBa98fU1X2s1Xe+fPnu8ULR9BHbYiISCWq7ksvERFxKRWEiIhYUkGIiIglFYSI\niFhSQYiIiCUVhFQ5p0+fpnHjxmzatKnc8s6dO/8i4zdu3JiSkpJfZKzKbNu2jS5duvDBBx/8rHH+\n/Oc/U1ZW9gulsjZ16lSOHj3q1OsQ96SCkCrpvvvuY+HChVy5csXVUX6SPXv2MHjwYPr16/ezxpk/\nf77TC2Ls2LE0bdrUqdch7kn/KCdVUkhICO3bt+ett95i1KhR5S5bt24dBw4c4M033wQgPj6eoUOH\n4unpyeLFi6lbty5HjhwhPDycxo0bs2PHDi5fvsySJUuoW7cucO3TNFNTU8nLy2PGjBk0atSI48eP\nM2PGDEpKSiguLubVV1/l4YcfJj4+niZNmnDs2DFWrlxZ7j+hd+/ezcKFC/Hx8aF69epMnjyZQ4cO\nsWfPHr744gs8PT0ZMGCAY/1vv/2W8ePHU1ZWRrVq1Zg2bRrBwcFMmDCBr7/+mqKiIsLDwxk3bhzz\n5s3ju+++45lnnmHBggUcP36chQsXYozBy8uLyZMnU69ePfbs2cOsWbOoWbMmUVFRrFq1ir1795KZ\nmcnYsWPJz8+nqKiIP/7xj0RHRzN//nxOnz7N2bNnGT16NDNmzGDo0KFERkaSmJjIX/7yF0pLS7n/\n/vuZMGECpaWljBw5kpycHEpKSujUqRNDhw79FR4F4nJGpIpJT083cXFxprCw0PTo0cOcOnXKGGNM\np06djDHGfPjhh2bkyJGO9ePi4sz+/ftNamqqiYiIMFlZWaagoMCEhYWZ9evXG2OMGT16tFm+fLkx\nxphGjRqZLVu2IGqjDgAABKdJREFUGGOMWbt2rRk+fLgxxpjf/e535rvvvjPGGHPs2DHz5JNPOsaf\nPXt2hZz5+fmmXbt25ty5c8YYYxITE82YMWMc17d27doK2wwaNMjs2rXLGGPM5s2bzfLly82lS5dM\nYmKiY52YmBjz1VdfObIWFxeb/Px8061bN5OVlWWMMWbHjh3m+eefN2VlZaZDhw7m2LFjxhhj3nzz\nTRMVFWWMMWb8+PFmyZIlxhhjMjMzTWRkpMnNzTXz5s0zsbGxpqysrNz+O3z4sImPj3csnzp1qvnf\n//1fs337djN48GBjjDGlpaVmxYoVprS09Cb3oPxW6AhCqiy73c6oUaOYOnUqy5Ytu61tGjZs6PgY\ng1q1atGiRQvg2kd0XD9d1a5dOwAiIiJ49913uXjxIt988w1jx451rHPlyhXH9E5ERESF6/r2228J\nCgpyHJW0bt2aNWvW3DTf3/72N1q3bg1c+yhwuPZJpOfOnWPAgAHY7XYyMjLIysoqt92JEyfIyMhg\n+PDhjm1sNhtZWVnk5+fTpEkTAGJiYti4cSMAhw8fZuDAgcC1DzmsU6cO33zzDQDh4eEVPgH34MGD\n/OMf/2DQoEHAtS+78fLyokePHsybN48XXniBDh060K9fvyr98Sbyy1FBSJXWoUMH3n//fXbs2OFY\nduMTW3FxsePnGz8I7/rfzXWfKvPDE5wxBpvNht1ux9vbm8TERMsc3t7eFZbdmOOHsW7lxnMK//d/\n/8eRI0d477338PLyok+fPhW2sdvthIaGVsh38eLFctd5/e21yvLDMqvbY7fb6dy5M6+++mqFyzZu\n3MihQ4f46KOP6Nu3L+vXr6/wPQfy26OXAVLlJSQkMGvWLMf3IteoUYPvv/8euPYEeeLEiR895g9f\nNJSWlkajRo3w9/fnnnvuYc+ePcC1L3VZsGDBTce47777uHjxImfPnnWMGR4eftNtIiIiHN9LvWXL\nFmbPns3Fixdp0KABXl5eHD16lH/84x+O22qz2SgpKeG+++4jKyuLv//97wB89tlnJCUlUbt2bTw8\nPPj6668Byn3/eXh4uOO6zp8/z4ULF2jQoMFNs+3du5e8vDwA3nvvPQ4dOsQnn3zC7t27+dd//VdG\njRqFr68vFy9evOntlN8GHUFIlXfvvfcSExPD4sWLgWvTQ8uWLaN///40bNjQMY10uzw9PTlx4gRr\n1qwhKyuLmTNnAtc+cXfKlCm88847lJSU3PIb9nx8fJg6dSovvfQSdrsdX1/fW37n9Pjx4xk/fjyr\nV6/Gy8vL8X3lQ4YMIS4ujoiICJ599lmmTJnC2rVriYqKom/fvixatIiZM2cyduxYqlWrBsBrr72G\nh4cHCQkJDBs2jNDQUFq2bImX17U/6xEjRjB27Fji4+MpLCxk8uTJ+Pn5VZotLCyMp556ivj4eKpV\nq0ZISAh9+vTh0qVLjBkzhqVLl+Lp6Un79u25++67b3t/i/vSp7mKuLmdO3fSuHFj6tWrx/bt20lK\nSrrtczYiN6MjCBE3V1ZWxvDhw6lRowalpaVV5rsQxP3pCEJERCzpJLWIiFhSQYiIiCUVhIiIWFJB\niIiIJRWEiIhY+n+3c1BC1nCZDAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb6fd18cc18>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Number of categories: 90. Average num: 1.235446792732666. Median num: 1.0\n",
            "Number of Train categories: 90. Average num: 1.2336208006178402. Median num: 1.0\n",
            "Number of Test categories: 90. Average num: 1.2401457436237164. Median num: 1.0\n",
            "Doc with 1 cat: 9160 (84.91%).Train doc with 1 cat: 6577 (84.66%). Test doc with 1 cat: 2583 (85.56%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "dnejQCXWZLoW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3551
        },
        "outputId": "34e2ea3b-363f-4eea-d0f6-a0fef39432cf"
      },
      "cell_type": "code",
      "source": [
        "from collections import defaultdict\n",
        "import numpy as np\n",
        "#\n",
        "# Keep trace of the number of different categories for documents having only one category\n",
        "#\n",
        "cat_one = set()\n",
        "cat_one_train = set()\n",
        "cat_one_test = set()\n",
        "\n",
        "cat_count = defaultdict(lambda: 0)\n",
        "\n",
        "#\n",
        "# For all docs\n",
        "#\n",
        "for fileid in fileids:\n",
        "  for category in reuters.categories(fileid):\n",
        "    cat_count[category] = cat_count[category] + 1\n",
        "\n",
        "width = .75\n",
        "x = list(cat_count.keys())\n",
        "y = list(cat_count.values())\n",
        "ind = np.arange(len(y))\n",
        "plt.barh(ind,y,width,tick_label=x)\n",
        "plt.title(\"Number of docs per categories\")\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "#\n",
        "# For train docs\n",
        "#\n",
        "for fileid in trainids:\n",
        "  for category in reuters.categories(fileid):\n",
        "    cat_count[category] = cat_count[category] + 1\n",
        "\n",
        "width = .75\n",
        "x = list(cat_count.keys())\n",
        "y = list(cat_count.values())\n",
        "ind = np.arange(len(y))\n",
        "plt.barh(ind,y,width,tick_label=x)\n",
        "plt.title(\"Number of Train docs per categories\")\n",
        "plt.show()\n",
        "\n",
        "#\n",
        "# For test docs\n",
        "#\n",
        "for fileid in testids:\n",
        "  for category in reuters.categories(fileid):\n",
        "    cat_count[category] = cat_count[category] + 1\n",
        "\n",
        "width = .75\n",
        "x = list(cat_count.keys())\n",
        "y = list(cat_count.values())\n",
        "ind = np.arange(len(y))\n",
        "plt.barh(ind,y,width,tick_label=x)\n",
        "plt.title(\"Number of Test docs per categories\")\n",
        "plt.show()\n",
        "\n",
        "#\n",
        "# For all docs with only one category docs\n",
        "#\n",
        "for fileid in fileids:\n",
        "  if len(reuters.categories(fileid)) > 1:\n",
        "    pass\n",
        "  else:\n",
        "    for category in reuters.categories(fileid):\n",
        "      cat_count[category] = cat_count[category] + 1\n",
        "\n",
        "width = .75\n",
        "x = list(cat_count.keys())\n",
        "y = list(cat_count.values())\n",
        "ind = np.arange(len(y))\n",
        "plt.barh(ind,y,width,tick_label=x)\n",
        "plt.title(\"Number of Test docs per categories with only one category docs\")\n",
        "plt.show()\n",
        "\n",
        "#\n",
        "# For train docs with only one category docs\n",
        "#\n",
        "for fileid in trainids:\n",
        "  if len(reuters.categories(fileid)) > 1:\n",
        "    pass\n",
        "  else:\n",
        "    for category in reuters.categories(fileid):\n",
        "      cat_count[category] = cat_count[category] + 1\n",
        "\n",
        "width = .75\n",
        "x = list(cat_count.keys())\n",
        "y = list(cat_count.values())\n",
        "ind = np.arange(len(y))\n",
        "plt.barh(ind,y,width,tick_label=x)\n",
        "plt.title(\"Number of Train docs per categories with only one category docs\")\n",
        "plt.show()\n",
        "\n",
        "#\n",
        "# For test docs with only one category docs\n",
        "#\n",
        "for fileid in testids:\n",
        "  if len(reuters.categories(fileid)) > 1:\n",
        "    pass\n",
        "  else:\n",
        "    for category in reuters.categories(fileid):\n",
        "      cat_count[category] = cat_count[category] + 1\n",
        "\n",
        "width = .75\n",
        "x = list(cat_count.keys())\n",
        "y = list(cat_count.values())\n",
        "ind = np.arange(len(y))\n",
        "plt.barh(ind,y,width,tick_label=x)\n",
        "plt.title(\"Number of Test docs per categories with only one category docs\")\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaUAAAJNCAYAAACRLWrcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb6dd6ae978>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaUAAAJNCAYAAACRLWrcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nURJCCKEYUpSEEEIohhQlIYQQivH/AJjCYNvICYvQAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb6fc7bda58>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaUAAAJNCAYAAACRLWrcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZkhREkIIUWZIURJCCFFm/B98V3UiV7ur8gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb6dd316358>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAJNCAYAAACLEYpSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb6de1d7cf8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAasAAAJNCAYAAACP5FpvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPwQhsV/vR5bYAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb6dd34e588>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAJNCAYAAACLEYpSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fb6dfccd978>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "YnkfqZKnoJJZ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Data processing"
      ]
    },
    {
      "metadata": {
        "id": "9wTPx9PfJXwa",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "MgDNZW6P9MXJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Flags definitions"
      ]
    },
    {
      "metadata": {
        "id": "FV7gTanC9Aw9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "# Data loading params\n",
        "tf.flags.DEFINE_float(\"dev_sample_percentage\", .1, \"Percentage of the training data to use for validation\")\n",
        "tf.flags.DEFINE_string(\"positive_data_file\", \"./data/rt-polaritydata/rt-polarity.pos\", \"Data source for the positive data.\")\n",
        "tf.flags.DEFINE_string(\"negative_data_file\", \"./data/rt-polaritydata/rt-polarity.neg\", \"Data source for the negative data.\")\n",
        "tf.flags.DEFINE_string(\"pre_trained_w2v\", \"./GoogleNews-vectors-negative300.bin\", \"Binary file containing pre-trained.\")\n",
        "\n",
        "# Model Hyperparameters\n",
        "tf.flags.DEFINE_integer(\"embedding_dim\", 128, \"Dimensionality of character embedding (default: 128)\")\n",
        "tf.flags.DEFINE_string(\"filter_sizes\", \"3,4,5\", \"Comma-separated filter sizes (default: '3,4,5')\")\n",
        "tf.flags.DEFINE_integer(\"num_filters\", 128, \"Number of filters per filter size (default: 128)\")\n",
        "tf.flags.DEFINE_float(\"dropout_keep_prob\", 0.5, \"Dropout keep probability (default: 0.5)\")\n",
        "tf.flags.DEFINE_float(\"l2_reg_lambda\", 0.0, \"L2 regularization lambda (default: 0.0)\")\n",
        "\n",
        "# Training parameters\n",
        "tf.flags.DEFINE_integer(\"batch_size\", 64, \"Batch Size (default: 64)\")\n",
        "tf.flags.DEFINE_integer(\"num_epochs\", 400, \"Number of training epochs (default: 200)\")\n",
        "tf.flags.DEFINE_integer(\"evaluate_every\", 100, \"Evaluate model on dev set after this many steps (default: 100)\")\n",
        "tf.flags.DEFINE_integer(\"checkpoint_every\", 100, \"Save model after this many steps (default: 100)\")\n",
        "tf.flags.DEFINE_integer(\"num_checkpoints\", 5, \"Number of checkpoints to store (default: 5)\")\n",
        "# Misc Parameters\n",
        "tf.flags.DEFINE_boolean(\"allow_soft_placement\", True, \"Allow device soft device placement\")\n",
        "tf.flags.DEFINE_boolean(\"log_device_placement\", False, \"Log placement of ops on devices\")\n",
        "\n",
        "# Define flags\n",
        "FLAGS = tf.flags.FLAGS"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "F8fm8oFA9si6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Data helper"
      ]
    },
    {
      "metadata": {
        "id": "VED_TTQx9vcd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from nltk.corpus import reuters\n",
        "import nltk\n",
        "import re\n",
        "from contextlib import suppress\n",
        "\n",
        "def clean_str(string):\n",
        "    \"\"\"\n",
        "    Tokenization/string cleaning for all datasets except for SST.\n",
        "    Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py\n",
        "    \"\"\"\n",
        "    string = re.sub(r\"[^A-Za-z0-9(),!?\\'\\`]\", \" \", string)\n",
        "    string = re.sub(r\"\\'s\", \" \\'s\", string)\n",
        "    string = re.sub(r\"\\'ve\", \" \\'ve\", string)\n",
        "    string = re.sub(r\"n\\'t\", \" n\\'t\", string)\n",
        "    string = re.sub(r\"\\'re\", \" \\'re\", string)\n",
        "    string = re.sub(r\"\\'d\", \" \\'d\", string)\n",
        "    string = re.sub(r\"\\'ll\", \" \\'ll\", string)\n",
        "    string = re.sub(r\",\", \" , \", string)\n",
        "    string = re.sub(r\"!\", \" ! \", string)\n",
        "    string = re.sub(r\"\\(\", \" \\( \", string)\n",
        "    string = re.sub(r\"\\)\", \" \\) \", string)\n",
        "    string = re.sub(r\"\\?\", \" \\? \", string)\n",
        "    string = re.sub(r\"\\s{2,}\", \" \", string)\n",
        "    return string.strip().lower()\n",
        "  \n",
        "def batch_iter(data, batch_size, num_epochs, shuffle=True):\n",
        "  \"\"\"\n",
        "  Generate a batch iterator for a dataset\n",
        "  \"\"\"\n",
        "  data = np.array(data)\n",
        "  data_size = len(data)\n",
        "  num_batches_per_epoch = int((data_size-1)/batch_size) + 1\n",
        "  for epoch in range(num_epochs):\n",
        "    if shuffle:\n",
        "      shuffle_indices = np.random.permutation(np.arange(data_size))\n",
        "      shuffled_data = data[shuffle_indices]\n",
        "    else:\n",
        "      shuffled_data = data\n",
        "    for batch_num in range(num_batches_per_epoch):\n",
        "      start_index = batch_num * batch_size\n",
        "      end_index = min((batch_num + 1) * batch_size, data_size)\n",
        "      yield shuffled_data[start_index:end_index]\n",
        "\n",
        "def load_data():\n",
        "  \"\"\"Load Reuters-21578 data\n",
        "  \n",
        "  Returns:\n",
        "    X (np.Array<Array<String>>): List of each documents represented as a list \n",
        "                              of String. Shape is (10788, 400)\n",
        "    y (np.Array<Array<Int>>): Corresponding label of each document. Each label \n",
        "                           is a list containing 1 if the label is assigned or 0.\n",
        "                           Its shape is (10788, 90)\n",
        "  \"\"\"\n",
        "  \n",
        "  \"\"\"import os.path\n",
        "  if os.path.exists(\"Xtext\") and os.path.exists(\"ylabels\"):\n",
        "    print(\"Xtext and ylabels found, loading files...\")\n",
        "    return np.load(\"Xtext\"), np.load(\"ylabels\")\"\"\"\n",
        "  \n",
        "  nltk.download('reuters')\n",
        "  X = []\n",
        "  y = []\n",
        "  doc_size = 400\n",
        "  \n",
        "  # Maps categories to uniq indexes from 0 to len(category)\n",
        "  categories_dict = {}\n",
        "  for idx, category in enumerate(reuters.categories()):\n",
        "    categories_dict[category] = idx\n",
        "  \n",
        "  # Take each doc, add it to 'X' generates its corresponding 'yi' and add it to\n",
        "  # the labels array.\n",
        "  for docid in reuters.fileids():\n",
        "    # We keep only docs with one assigned category (~85% of the dataset)\n",
        "    #if len(reuters.categories(docid)) > 1:\n",
        "    #  continue\n",
        "    # Here we apply `clean_str` and keep only words with more than 1 character\n",
        "    X_i = list(\n",
        "        filter(lambda word: len(word) > 1,\n",
        "        map(lambda word: clean_str(word), reuters.words(docid))))\n",
        "    # We cut or Pad to have fixed lenght documents\n",
        "    # The word `<PAD>` will have its own index in our dictionnary. Maybe having\n",
        "    # it as 0 would be better.\n",
        "    X_i = [ X_i[i] if i < len(X_i) else \"<PAD>\" for i in range(doc_size) ]\n",
        "    # Here we build the label `np.Array`. For each corresponding category we\n",
        "    # replace 0 by 1\n",
        "    X.append(X_i)\n",
        "    yi = np.zeros(len(categories_dict))\n",
        "    for category in reuters.categories(docid):\n",
        "      yi[categories_dict[category]] = 1\n",
        "    y.append(yi)\n",
        "    \n",
        "    #np.save(\"Xtext\", np.array(X))\n",
        "    #np.save(\"ylabels\", np.array(y))\n",
        "    \n",
        "  return np.array(X), np.array(y)\n",
        "\n",
        "def loadRCV1():\n",
        "  \"\"\"Fetch RCV1 dataset\n",
        "  \n",
        "  Returns:\n",
        "    X (CSR sparse matrix): Features matrix of shape (804414, 47236)\n",
        "    y (CSR sparse matrix): Corresponding labels of shape (804414, 103)\n",
        "  \"\"\"\n",
        "  from sklearn.datasets import fetch_rcv1\n",
        "  rcv1 = fetch_rcv1()\n",
        "  return rcv1.data, rcv1.target\n",
        "\n",
        "def preprocessRCV1():\n",
        "  X_all, y_all = loadRCV1()\n",
        "  assert X_all.shape[0] == y_all.shape[0]\n",
        "  \n",
        "  x_train, x_dev = X_all[:23149], X_all[23149:]\n",
        "  y_train, y_dev = y_all[:23149], y_all[23149:]\n",
        "  \n",
        "  return x_train, y_train, np.zeros(47236), x_dev, y_dev\n",
        "\n",
        "def load_pre_tained_word2vec(vocabulary, filename):\n",
        "  \"\"\" Loads pre-trained word2vec fom a binary file.\n",
        "  Taken from https://github.com/cahya-wirawan/cnn-text-classification-tf/blob\n",
        "  /master/data_helpers.py\n",
        "  \n",
        "  Params:\n",
        "    vocabulary (Dict<String:Int>): Dictionnary mapping words to ints\n",
        "    filename (String): Path to the binary file\n",
        "    \n",
        "  Returns:\n",
        "      embedding_vectors (Array<Float32>): A 2D-matrix of shape \n",
        "                                          voc_size X embedding_size\n",
        "  \"\"\"\n",
        "  encoding = 'utf-8'\n",
        "  with open(filename, \"rb\") as f:\n",
        "    header = f.readline()\n",
        "    # First line of the file contains the vocabulary size and the vector size\n",
        "    # in the form `300000 300`\n",
        "    vocab_size, vector_size = map(int, header.split())\n",
        "    # Initialize matrix with random uniform\n",
        "    embedding_vectors = np.random.uniform(-0.25, 0.25, (len(vocabulary), vector_size))\n",
        "    binary_len = np.dtype('float32').itemsize * vector_size\n",
        "    # We iterate for each words in the pre-trained file\n",
        "    for line_no in range(vocab_size):\n",
        "      word = []\n",
        "      # We get the word at the line\n",
        "      while True:\n",
        "        # Read a character\n",
        "        ch = f.read(1)\n",
        "        if ch == b' ':\n",
        "          break\n",
        "        if ch == b'':\n",
        "          raise EOFError(\"unexpected end of input; is count incorrect or file\\\n",
        "                          otherwise damaged?\")\n",
        "        if ch != b'\\n':\n",
        "          word.append(ch)\n",
        "      # We build the word from the list of characters\n",
        "      word = str(b''.join(word), encoding=encoding, errors='strict')\n",
        "      # We get the word index from our given dictionnary\n",
        "      idx = vocabulary.get(word, 0)\n",
        "      if idx != 0:\n",
        "        # If the word exists in our corpus, we set its embedding value\n",
        "        embedding_vectors[idx] = np.fromstring(f.read(binary_len), dtype='float32')\n",
        "      else:\n",
        "        # We manually move the cursor relatively (1) because we won't read\n",
        "        f.seek(binary_len, 1)\n",
        "    f.close()\n",
        "    return embedding_vectors\n",
        "  \n",
        "\n",
        "def preprocess():\n",
        "  \"\"\"This method builds the train and validation datasets\n",
        "  Text must be first set to a fixed size. It is either padded or cutted. Padding\n",
        "  is made with <PAD> token.\n",
        "  We use a fixed dictionnary size and each word is replaced by its dictionnary\n",
        "  index. We end up transforming vector of words to vector of integers \n",
        "  correcponding to the indexes of the dictionnary.\n",
        "\n",
        "  Returns:\n",
        "    x_train (Array<Array<Int>>): train dataset\n",
        "    y_train (Array<Array<Int>>): corresponding train labels\n",
        "    x_dev (Array<Array<Int>>): validation dataset\n",
        "    y_dev (Array<Array<Int>>): corresponding validation labels\n",
        "  \"\"\"\n",
        "\n",
        "  X_text, y = load_data()\n",
        "  assert len(X_text) == len(y)\n",
        "  \n",
        "\n",
        "  # Build a set containing each uniq words\n",
        "  voc = set()\n",
        "  [voc.add(word) for doc in X_text for word in doc]\n",
        "  # Build dictionnary, each key is a word and value its index\n",
        "  voc_dict = {}\n",
        "  for idx, word in enumerate(voc):\n",
        "    voc_dict[word] = idx\n",
        "  # We build a new dataset where each word is replaced by its dictionnary index\n",
        "  X = np.array([ [ voc_dict.get(word, 0) for word in doc ] for doc in X_text ])\n",
        "    \n",
        "  # Shuffle data\n",
        "  np.random.seed(10)\n",
        "  shuffle_indices = np.random.permutation(np.arange(len(y)))\n",
        "  x_shuffled = X[shuffle_indices]\n",
        "  y_shuffled = y[shuffle_indices]\n",
        "  \n",
        "  # Split train/test set\n",
        "  # TODO: This is very crude, should use cross-validation\n",
        "  dev_sample_index = -1 * int(0.1 * float(len(y)))\n",
        "  x_train, x_dev = x_shuffled[:dev_sample_index], x_shuffled[dev_sample_index:]\n",
        "  y_train, y_dev = y_shuffled[:dev_sample_index], y_shuffled[dev_sample_index:]\n",
        "\n",
        "  del X, y, x_shuffled, y_shuffled, voc\n",
        "\n",
        "  print(\"Vocabulary Size: {:d}\".format(len(voc_dict)))\n",
        "  print(\"Train/Dev split: {:d}/{:d}\".format(len(y_train), len(y_dev)))\n",
        "  return x_train, y_train, voc_dict, x_dev, y_dev"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NCCrMQ7O91Vc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Training"
      ]
    },
    {
      "metadata": {
        "id": "ECL2uL-KoTLv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 65962
        },
        "outputId": "fdeedb21-1a54-4ff4-9aa7-2925a678467d"
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import time\n",
        "import datetime\n",
        "import os\n",
        "from tensorflow.contrib.tensorboard.plugins import projector\n",
        "from operator import itemgetter\n",
        "\n",
        "\n",
        "class TextCNN(object):\n",
        "  \"\"\"\n",
        "  A CNN for text classification.\n",
        "  Uses an embedding layer, followed by a convolutional, max-pooling and softmax\n",
        "  layer.\n",
        "  \"\"\"\n",
        "  \n",
        "  def __init__(self, text_len, num_classes, \n",
        "               voc_size, embedding_size, \n",
        "               filter_sizes, num_filters):\n",
        "    \"\"\"Initialization method.\n",
        "\n",
        "    Args:\n",
        "      text_len (Int): Indicates the fixed lenght of the text\n",
        "      num_classes (Int): The number of classes or labels\n",
        "      voc_size (Int): Size of vocabulary\n",
        "      embedding_size (Int): Embedding dimensionality\n",
        "      filter_sizes (Array<Int>): Number of words we want our convolutional\n",
        "                                 filter to cover.\n",
        "      num_filters (Int): Number of filter per filter_size\n",
        "\n",
        "  \"\"\"\n",
        "  \n",
        "    # Tensorflow placeholder for input, output and dropout\n",
        "    # Input is an addition of 1-hot vectors?\n",
        "    self.input_x = tf.placeholder(tf.int32, [None, text_len], \n",
        "                                  name=\"input_x\")\n",
        "    self.input_y = tf.placeholder(tf.float32, [None, num_classes],\n",
        "                                  name=\"input_y\")\n",
        "    self.dropout_keep_prob = tf.placeholder(tf.float32, \n",
        "                                            name=\"dropout_keep_prob\")\n",
        "    \n",
        "    # Keeping track of l2 regularization loss (optional)\n",
        "    l2_loss = tf.constant(0.0)\n",
        "    \n",
        "    # Embedding layer\n",
        "    # Embedding is not yet supported on GPU\n",
        "    with tf.name_scope(\"embedding\"):\n",
        "      # W is our embedded matrix that we learn\n",
        "      self.W = tf.Variable(tf.random_uniform([voc_size, embedding_size], -1.0, 1.0),\n",
        "                      name=\"W\")\n",
        "      # This results in a [None, text_len, embedding_size] 3D tensor\n",
        "      # Will lookup input_x (the ids) on W\n",
        "      self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x)\n",
        "      # CNN is expecting 4D tensor but we don't have color chanel, so let's add\n",
        "      # a dimension. We end with [None, text_len, embedding_size, 1] 4D tensor\n",
        "      self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)\n",
        "      \n",
        "    pooled_outputs = []\n",
        "    for i, filter_size in enumerate(filter_sizes):\n",
        "      with tf.name_scope(\"conv-maxpool-%s\" % filter_size):\n",
        "        # Convolution layer\n",
        "        filter_shape = [filter_size, embedding_size, 1, num_filters]\n",
        "        # Weights and bias for each different filter_size\n",
        "        W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name=\"W\")\n",
        "        b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name=\"b\")\n",
        "        conv = tf.nn.conv2d(\n",
        "          self.embedded_chars_expanded, # Input\n",
        "          W, # Filter\n",
        "          [1,1,1,1], # Strides\n",
        "          'VALID', # Padding, performs a narrow convolution\n",
        "          name=\"conv\"\n",
        "        )\n",
        "        # Apply nonlinearity\n",
        "        h = tf.nn.relu(tf.nn.bias_add(conv, b), name=\"relu\")\n",
        "        # Max-pooling over the outputs\n",
        "        pooled = tf.nn.max_pool(\n",
        "          h, # Value\n",
        "          [1, text_len - filter_size + 1, 1, 1], # ksize. Window size\n",
        "          [1, 1, 1, 1], # Strides\n",
        "          'VALID', # Padding\n",
        "          name=\"pool\"\n",
        "        )\n",
        "        pooled_outputs.append(pooled)\n",
        "    \n",
        "    # Combine all the pooled features\n",
        "    num_filters_total = num_filters * len(filter_sizes)\n",
        "    self.h_pool = tf.concat(pooled_outputs, 3)\n",
        "    # Here we flatten to 1 dimension\n",
        "    self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])\n",
        "    \n",
        "    # Add dropout\n",
        "    with tf.name_scope(\"dropout\"):\n",
        "      self.h_drop = tf.nn.dropout(self.h_pool_flat, self.dropout_keep_prob)\n",
        "    \n",
        "    # Score and prediction\n",
        "    with tf.name_scope(\"output\"):\n",
        "      W = tf.Variable(tf.truncated_normal([num_filters_total, num_classes],\n",
        "                                         stddev=0.1), name=\"W\")\n",
        "      b = tf.Variable(tf.constant(0.1, shape=[num_classes]), name=\"b\")\n",
        "      # Using L2 normalization (optional)\n",
        "      l2_loss += tf.nn.l2_loss(W)\n",
        "      l2_loss += tf.nn.l2_loss(b)\n",
        "      self.scores = tf.nn.xw_plus_b(self.h_drop, W, b, name=\"scores\")\n",
        "      self.predictions = tf.argmax(self.scores, 1, name=\"predictions\")\n",
        "      \n",
        "    # Loss and Accuracy\n",
        "    # Calculate mean cross-entropy loss\n",
        "    with tf.name_scope(\"loss\"):\n",
        "      losses = tf.nn.sigmoid_cross_entropy_with_logits(\n",
        "          logits=self.scores, labels=self.input_y)\n",
        "      self.loss = tf.reduce_mean(losses) + 0.1 * l2_loss\n",
        "      \n",
        "    # Calculate accuracy\n",
        "    with tf.name_scope(\"accuracy\"):\n",
        "      correct_predictions = tf.equal(self.predictions, \n",
        "                                        tf.argmax(self.input_y, 1)) # Input, Axis\n",
        "      self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, \"float\"), \n",
        "                                     name=\"accuracy\")\n",
        "\n",
        "def train(x_train, y_train, voc_dict, x_dev, y_dev):\n",
        "  with tf.Graph().as_default():\n",
        "    session_conf = tf.ConfigProto(\n",
        "      allow_soft_placement=FLAGS.allow_soft_placement, # Device fallback\n",
        "      log_device_placement=FLAGS.log_device_placement # Log on devices\n",
        "    )\n",
        "    sess = tf.Session(config=session_conf)\n",
        "    with sess.as_default():\n",
        "      # \n",
        "      # Instantiate our class that defines the CNN\n",
        "      #\n",
        "      cnn = TextCNN(\n",
        "        text_len=x_train.shape[1],\n",
        "        num_classes=y_train.shape[1],\n",
        "        voc_size=len(voc_dict),\n",
        "        embedding_size=300,#FLAGS.embedding_dim,\n",
        "        filter_sizes=list(map(int, FLAGS.filter_sizes.split(\",\"))),\n",
        "        num_filters=FLAGS.num_filters\n",
        "      )\n",
        "\n",
        "      # Optimizer\n",
        "      global_step = tf.Variable(0, name=\"global_step\", trainable=False)\n",
        "      optimizer = tf.train.AdamOptimizer(1e-4)\n",
        "      grads_and_vars = optimizer.compute_gradients(cnn.loss)\n",
        "      train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)\n",
        "\n",
        "      # Summary\n",
        "      # Output directory for models and summaries\n",
        "      timestamp = str(int(time.time()))\n",
        "      out_dir = os.path.abspath(os.path.join(os.path.curdir, \"runs\", timestamp))\n",
        "      print(\"Writing to {}\\n\".format(out_dir))\n",
        "\n",
        "      # Summaries for loss and accuracy\n",
        "      loss_summary = tf.summary.scalar(\"loss\", cnn.loss)\n",
        "      acc_summary = tf.summary.scalar(\"accuracy\", cnn.accuracy)\n",
        "\n",
        "      # Train Summaries\n",
        "      train_summary_op = tf.summary.merge([loss_summary, acc_summary])\n",
        "      train_summary_dir = os.path.join(out_dir, \"summaries\", \"train\")\n",
        "      train_summary_writer = tf.summary.FileWriter(train_summary_dir, sess.graph)\n",
        "      train_summary_writer.add_graph(sess.graph)\n",
        "\n",
        "      # Dev Summaries\n",
        "      dev_summary_op = tf.summary.merge([loss_summary, acc_summary])\n",
        "      dev_summary_dir = os.path.join(out_dir, \"summaries\", \"dev\")\n",
        "      dev_summary_writer = tf.summary.FileWriter(dev_summary_dir, sess.graph)\n",
        "\n",
        "      # Checkpointing\n",
        "      checkpoint_dir = os.path.abspath(os.path.join(out_dir, \"checkpoints\"))\n",
        "      checkpoint_prefix = os.path.join(checkpoint_dir, \"models\")\n",
        "      if not os.path.exists(checkpoint_dir):\n",
        "        os.makedirs(checkpoint_dir)\n",
        "      saver = tf.train.Saver(tf.global_variables())\n",
        "      \n",
        "      # Projector\n",
        "      config = projector.ProjectorConfig()\n",
        "      embedding = config.embeddings.add()\n",
        "      embedding.tensor_name = cnn.W.name\n",
        "      # Save labels for projector. Useful to have word instead of integers\n",
        "      projector_metadata_path = os.path.join(out_dir, \"projector_metadata.tsv\")\n",
        "      with open(projector_metadata_path, \"a\") as metadata_file:\n",
        "        metadata_file.write(\"Word\\tIndex\")\n",
        "        for word, index in sorted(voc_dict.items(), \n",
        "                                  key=itemgetter(1), reverse=False):\n",
        "          metadata_file.write(\"\\n{}\\t{}\".format(word, index))\n",
        "      embedding.metadata_path = projector_metadata_path\n",
        "      projector.visualize_embeddings(dev_summary_writer, config)\n",
        "\n",
        "      # Initialize variables\n",
        "      sess.run(tf.global_variables_initializer())\n",
        "      \n",
        "      # \n",
        "      # Use of pre-trained word2vec\n",
        "      #\n",
        "      initW = None\n",
        "      print(\"Load word2vec at {}\".format(FLAGS.pre_trained_w2v))\n",
        "      initW = load_pre_tained_word2vec(voc_dict, FLAGS.pre_trained_w2v)\n",
        "      sess.run(cnn.W.assign(initW))\n",
        "      \n",
        "      # Define a train step\n",
        "      def train_step(x_batch, y_batch):\n",
        "        \"\"\"\n",
        "        A single training step\n",
        "        \"\"\"\n",
        "        feed_dict = {\n",
        "            cnn.input_x: x_batch,\n",
        "            cnn.input_y: y_batch,\n",
        "            cnn.dropout_keep_prob: FLAGS.dropout_keep_prob\n",
        "        }\n",
        "        _, step, summaries, loss, accuracy = sess.run(\n",
        "            # Graph elements at its leaves\n",
        "          [train_op, global_step, train_summary_op, cnn.loss, cnn.accuracy],\n",
        "          feed_dict\n",
        "        )\n",
        "        time_str = datetime.datetime.now().isoformat()\n",
        "        #print(\"{}: step {}, loss {:g}, acc {:g}\".format(time_str, step, loss, accuracy))\n",
        "        train_summary_writer.add_summary(summaries, step)\n",
        "\n",
        "      def dev_step(x_batch, y_batch, writer=None):\n",
        "        \"\"\"\n",
        "        Evaluates model on a dev set\n",
        "        \"\"\"\n",
        "        feed_dict =  {\n",
        "            cnn.input_x: x_batch,\n",
        "            cnn.input_y: y_batch,\n",
        "            cnn.dropout_keep_prob: 1.0\n",
        "        }\n",
        "        step, summaries, loss, accuracy = sess.run(\n",
        "          [global_step, dev_summary_op, cnn.loss, cnn.accuracy],\n",
        "          feed_dict\n",
        "        )\n",
        "        time_str = datetime.datetime.now().isoformat()\n",
        "        print(\"{}: step {}, loss {:g}, acc {:g}\".format(time_str, step, loss, accuracy))\n",
        "        if writer:\n",
        "          writer.add_summary(summaries, step)\n",
        "\n",
        "      # \n",
        "      # Generate batches\n",
        "      # Will loop on the number of epochs and successively yield batches\n",
        "      #\n",
        "      batches = batch_iter(\n",
        "        list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs\n",
        "      )\n",
        "\n",
        "      # \n",
        "      # This actually initiate the learning\n",
        "      # We loop for each epoch on each batch. See `batch_iter`\n",
        "      # \n",
        "      for batch in batches:\n",
        "        x_batch, y_batch = zip(*batch)\n",
        "        train_step(x_batch, y_batch)\n",
        "        current_step = tf.train.global_step(sess, global_step)\n",
        "        if current_step % FLAGS.evaluate_every == 0:\n",
        "          print(\"\\nEvaluation:\")\n",
        "          dev_step(x_dev, y_dev, writer=dev_summary_writer)\n",
        "          print(\"\")\n",
        "        if current_step % FLAGS.checkpoint_every == 0:\n",
        "          path = saver.save(sess, checkpoint_prefix, global_step=current_step)\n",
        "          print(\"Saved model checkpoint to {}\\n\".format(path))\n",
        "        \n",
        "        \n",
        "def main(argv=None):\n",
        "    x_train, y_train, voc_dict, x_dev, y_dev = preprocess()\n",
        "    train(x_train, y_train, voc_dict, x_dev, y_dev)\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    tf.app.run()\n",
        "    "
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package reuters to /content/nltk_data...\n",
            "[nltk_data]   Package reuters is already up-to-date!\n",
            "Vocabulary Size: 30097\n",
            "Train/Dev split: 9710/1078\n",
            "Writing to /content/runs/1527494908\n",
            "\n",
            "Load word2vec at ./GoogleNews-vectors-negative300.bin\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:159: DeprecationWarning: The binary mode of fromstring is deprecated, as it behaves surprisingly on unicode inputs. Use frombuffer instead\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:09:08.021632: step 100, loss 11.849, acc 0.168831\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:09:16.669008: step 200, loss 9.77894, acc 0.335807\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:09:25.304241: step 300, loss 8.10011, acc 0.399814\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:09:33.909966: step 400, loss 6.71103, acc 0.397032\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:09:42.557110: step 500, loss 5.55678, acc 0.485158\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:09:51.145884: step 600, loss 4.59747, acc 0.51577\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:09:59.753649: step 700, loss 3.79884, acc 0.532468\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:10:08.364039: step 800, loss 3.13358, acc 0.550093\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:10:16.967566: step 900, loss 2.58062, acc 0.54731\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:10:25.544486: step 1000, loss 2.12022, acc 0.556586\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-1000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:10:34.158492: step 1100, loss 1.73818, acc 0.558442\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-1100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:10:42.752323: step 1200, loss 1.42078, acc 0.558442\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-1200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:10:51.385226: step 1300, loss 1.15832, acc 0.557514\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-1300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:11:00.007922: step 1400, loss 0.942435, acc 0.555659\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-1400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:11:08.636840: step 1500, loss 0.764769, acc 0.558442\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-1500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:11:17.265951: step 1600, loss 0.619607, acc 0.556586\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-1600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:11:25.853923: step 1700, loss 0.501303, acc 0.554731\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-1700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:11:34.453367: step 1800, loss 0.405548, acc 0.549165\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-1800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:11:43.076625: step 1900, loss 0.328602, acc 0.550093\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-1900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:11:51.671465: step 2000, loss 0.266696, acc 0.545455\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-2000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:12:00.304852: step 2100, loss 0.217564, acc 0.539889\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-2100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:12:08.883386: step 2200, loss 0.178837, acc 0.545455\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-2200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:12:17.469326: step 2300, loss 0.148315, acc 0.549165\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-2300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:12:26.073149: step 2400, loss 0.124408, acc 0.55102\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-2400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:12:34.717321: step 2500, loss 0.10578, acc 0.540816\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-2500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:12:43.269626: step 2600, loss 0.0916787, acc 0.546382\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-2600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:12:51.867102: step 2700, loss 0.0808357, acc 0.553803\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-2700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:13:00.471132: step 2800, loss 0.0726357, acc 0.546382\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-2800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:13:09.035197: step 2900, loss 0.0663514, acc 0.541744\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-2900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:13:17.626032: step 3000, loss 0.0617207, acc 0.550093\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-3000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:13:26.209180: step 3100, loss 0.0581919, acc 0.542672\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-3100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:13:34.746009: step 3200, loss 0.0554507, acc 0.543599\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-3200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:13:43.337228: step 3300, loss 0.0534745, acc 0.542672\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-3300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:13:51.969840: step 3400, loss 0.0520718, acc 0.550093\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-3400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:14:00.583123: step 3500, loss 0.0506659, acc 0.543599\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-3500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:14:09.226322: step 3600, loss 0.0496458, acc 0.544527\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-3600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:14:17.847381: step 3700, loss 0.0488504, acc 0.546382\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-3700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:14:26.467654: step 3800, loss 0.0482585, acc 0.546382\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-3800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:14:35.076721: step 3900, loss 0.0475657, acc 0.548238\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-3900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:14:43.681899: step 4000, loss 0.0471802, acc 0.549165\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-4000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:14:52.305630: step 4100, loss 0.0466343, acc 0.546382\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-4100\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:15:00.867816: step 4200, loss 0.0461135, acc 0.54731\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-4200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:15:09.450074: step 4300, loss 0.0457215, acc 0.55102\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-4300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:15:18.102344: step 4400, loss 0.0451685, acc 0.556586\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-4400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:15:26.695492: step 4500, loss 0.0446922, acc 0.546382\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-4500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:15:35.256353: step 4600, loss 0.0441831, acc 0.555659\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-4600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:15:43.812234: step 4700, loss 0.0439576, acc 0.572356\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-4700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:15:52.403722: step 4800, loss 0.0434845, acc 0.555659\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-4800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:16:00.976794: step 4900, loss 0.0430935, acc 0.579777\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-4900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:16:09.550573: step 5000, loss 0.0426738, acc 0.575139\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-5000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:16:18.135061: step 5100, loss 0.0423235, acc 0.600186\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-5100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:16:26.707582: step 5200, loss 0.0420482, acc 0.560297\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-5200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:16:35.349253: step 5300, loss 0.0416025, acc 0.61039\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-5300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:16:43.906516: step 5400, loss 0.0410499, acc 0.602968\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-5400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:16:52.496061: step 5500, loss 0.0408762, acc 0.62987\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-5500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:17:01.112948: step 5600, loss 0.0402585, acc 0.601113\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-5600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:17:09.711545: step 5700, loss 0.0397027, acc 0.612245\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-5700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:17:18.305368: step 5800, loss 0.0394426, acc 0.607607\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-5800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:17:26.890381: step 5900, loss 0.0389795, acc 0.631725\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-5900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:17:35.492070: step 6000, loss 0.0390739, acc 0.639147\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-6000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:17:44.091854: step 6100, loss 0.0383366, acc 0.639147\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-6100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T08:17:52.677815: step 6200, loss 0.0379563, acc 0.641002\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-6200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:18:01.290514: step 6300, loss 0.0374378, acc 0.64564\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-6300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:18:09.881901: step 6400, loss 0.0371291, acc 0.638219\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-6400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:18:18.502151: step 6500, loss 0.0367923, acc 0.638219\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-6500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:18:27.138550: step 6600, loss 0.0364126, acc 0.647495\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-6600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:18:35.725514: step 6700, loss 0.0362863, acc 0.638219\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-6700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:18:44.351831: step 6800, loss 0.0356945, acc 0.657699\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-6800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:18:52.943049: step 6900, loss 0.0353208, acc 0.653989\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-6900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:19:01.497367: step 7000, loss 0.0351364, acc 0.651206\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-7000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:19:10.098579: step 7100, loss 0.0347597, acc 0.659555\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-7100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:19:18.639606: step 7200, loss 0.034403, acc 0.66141\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-7200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:19:27.204040: step 7300, loss 0.0340616, acc 0.66141\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-7300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:19:35.804395: step 7400, loss 0.0336998, acc 0.670686\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-7400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:19:44.412424: step 7500, loss 0.0335194, acc 0.680891\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-7500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:19:52.971136: step 7600, loss 0.0330995, acc 0.658627\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-7600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:20:01.602803: step 7700, loss 0.0327783, acc 0.664193\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-7700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:20:10.190078: step 7800, loss 0.0325842, acc 0.679963\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-7800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:20:18.788803: step 7900, loss 0.0324103, acc 0.686456\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-7900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:20:27.387391: step 8000, loss 0.0320554, acc 0.679963\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-8000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:20:35.996981: step 8100, loss 0.0320062, acc 0.681818\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-8100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T08:20:44.567849: step 8200, loss 0.0315462, acc 0.687384\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-8200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:20:53.149231: step 8300, loss 0.0312626, acc 0.692022\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-8300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:21:01.714914: step 8400, loss 0.0311363, acc 0.679963\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-8400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:21:10.327713: step 8500, loss 0.0307999, acc 0.683673\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-8500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:21:18.876156: step 8600, loss 0.030279, acc 0.69295\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-8600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:21:27.432344: step 8700, loss 0.0303606, acc 0.693878\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-8700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:21:36.065173: step 8800, loss 0.0301127, acc 0.691095\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-8800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:21:44.597094: step 8900, loss 0.0297915, acc 0.690167\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-8900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:21:53.305182: step 9000, loss 0.0297331, acc 0.69295\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-9000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:22:01.903451: step 9100, loss 0.0294675, acc 0.700371\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-9100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:22:10.484118: step 9200, loss 0.0295071, acc 0.693878\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-9200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:22:19.098824: step 9300, loss 0.0291054, acc 0.71243\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-9300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:22:27.661108: step 9400, loss 0.0290025, acc 0.707792\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-9400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:22:36.253966: step 9500, loss 0.0286316, acc 0.706865\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-9500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:22:44.848606: step 9600, loss 0.0285438, acc 0.705937\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-9600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:22:53.411450: step 9700, loss 0.028597, acc 0.702226\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-9700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:23:01.976549: step 9800, loss 0.0281399, acc 0.701299\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-9800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:23:10.546281: step 9900, loss 0.0280818, acc 0.709647\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-9900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:23:19.135612: step 10000, loss 0.027953, acc 0.705009\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-10000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:23:27.706689: step 10100, loss 0.0278556, acc 0.716141\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-10100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T08:23:36.329183: step 10200, loss 0.0276268, acc 0.702226\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-10200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:23:44.902128: step 10300, loss 0.0275484, acc 0.705009\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-10300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:23:53.482680: step 10400, loss 0.0273846, acc 0.705937\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-10400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:24:02.083387: step 10500, loss 0.0275453, acc 0.718924\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-10500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:24:10.661717: step 10600, loss 0.0273878, acc 0.718924\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-10600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:24:19.251237: step 10700, loss 0.0271163, acc 0.723562\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-10700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:24:27.795205: step 10800, loss 0.0268402, acc 0.726345\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-10800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:24:36.411658: step 10900, loss 0.0269113, acc 0.723562\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-10900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:24:44.957036: step 11000, loss 0.0268529, acc 0.730056\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-11000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:24:53.523033: step 11100, loss 0.0266576, acc 0.70872\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-11100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:25:02.186967: step 11200, loss 0.0262914, acc 0.7282\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-11200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:25:10.748700: step 11300, loss 0.0265905, acc 0.70872\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-11300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:25:19.316959: step 11400, loss 0.0264531, acc 0.726345\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-11400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:25:27.908403: step 11500, loss 0.0260484, acc 0.710575\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-11500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:25:36.481394: step 11600, loss 0.0260825, acc 0.706865\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-11600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:25:45.079961: step 11700, loss 0.0260958, acc 0.720779\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-11700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:25:53.624312: step 11800, loss 0.0260499, acc 0.720779\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-11800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:26:02.272174: step 11900, loss 0.0264448, acc 0.705009\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-11900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:26:10.867161: step 12000, loss 0.0259079, acc 0.709647\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-12000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:26:19.470973: step 12100, loss 0.0258571, acc 0.720779\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-12100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T08:26:28.025929: step 12200, loss 0.0259995, acc 0.713358\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-12200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:26:36.646390: step 12300, loss 0.025416, acc 0.741187\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-12300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:26:45.205894: step 12400, loss 0.0252665, acc 0.726345\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-12400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:26:53.764942: step 12500, loss 0.0255049, acc 0.719852\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-12500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:27:02.395049: step 12600, loss 0.0255664, acc 0.730056\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-12600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:27:10.978665: step 12700, loss 0.0252585, acc 0.741187\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-12700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:27:19.571685: step 12800, loss 0.0253419, acc 0.741187\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-12800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:27:28.237029: step 12900, loss 0.0250708, acc 0.72449\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-12900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:27:36.849983: step 13000, loss 0.0250313, acc 0.735622\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-13000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:27:45.429078: step 13100, loss 0.0252161, acc 0.730983\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-13100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:27:54.012625: step 13200, loss 0.0250266, acc 0.727273\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-13200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:28:02.591715: step 13300, loss 0.0248247, acc 0.736549\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-13300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:28:11.181604: step 13400, loss 0.0247954, acc 0.74026\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-13400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:28:19.763222: step 13500, loss 0.0247569, acc 0.732839\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-13500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:28:28.349437: step 13600, loss 0.0247886, acc 0.739332\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-13600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:28:36.900303: step 13700, loss 0.0244721, acc 0.74397\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-13700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:28:45.513863: step 13800, loss 0.0245928, acc 0.730983\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-13800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:28:54.154925: step 13900, loss 0.0246147, acc 0.746753\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-13900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:29:02.710488: step 14000, loss 0.0245483, acc 0.731911\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-14000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:29:11.313952: step 14100, loss 0.0243919, acc 0.743043\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-14100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T08:29:19.903778: step 14200, loss 0.0242594, acc 0.74397\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-14200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:29:28.480856: step 14300, loss 0.0242967, acc 0.736549\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-14300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:29:37.082105: step 14400, loss 0.0243239, acc 0.745826\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-14400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:29:45.676031: step 14500, loss 0.0241096, acc 0.748609\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-14500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:29:54.297703: step 14600, loss 0.0238851, acc 0.753247\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-14600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:30:02.888352: step 14700, loss 0.0241643, acc 0.751391\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-14700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:30:11.456624: step 14800, loss 0.0242604, acc 0.738404\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-14800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:30:20.039549: step 14900, loss 0.0242588, acc 0.739332\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-14900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:30:28.665790: step 15000, loss 0.0239182, acc 0.734694\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-15000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:30:37.228813: step 15100, loss 0.0238102, acc 0.747681\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-15100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:30:45.784355: step 15200, loss 0.023826, acc 0.7282\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-15200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:30:54.374977: step 15300, loss 0.0238053, acc 0.742115\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-15300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:31:02.918415: step 15400, loss 0.0235292, acc 0.744898\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-15400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:31:11.484884: step 15500, loss 0.023319, acc 0.760668\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-15500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:31:20.093838: step 15600, loss 0.023255, acc 0.763451\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-15600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:31:28.675476: step 15700, loss 0.0231732, acc 0.756957\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-15700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:31:37.233020: step 15800, loss 0.0234019, acc 0.755102\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-15800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:31:45.791855: step 15900, loss 0.0232105, acc 0.753247\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-15900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:31:54.360295: step 16000, loss 0.0230122, acc 0.75974\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-16000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:32:02.979193: step 16100, loss 0.0228821, acc 0.754174\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-16100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T08:32:11.536537: step 16200, loss 0.0230093, acc 0.762523\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-16200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:32:20.116652: step 16300, loss 0.0229259, acc 0.755102\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-16300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:32:28.744649: step 16400, loss 0.023182, acc 0.767161\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-16400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:32:37.347325: step 16500, loss 0.0227642, acc 0.760668\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-16500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:32:45.943531: step 16600, loss 0.0229509, acc 0.755102\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-16600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:32:54.539748: step 16700, loss 0.0228264, acc 0.758813\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-16700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:33:03.155486: step 16800, loss 0.02275, acc 0.766234\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-16800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:33:11.721164: step 16900, loss 0.0222057, acc 0.763451\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-16900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:33:20.339323: step 17000, loss 0.0225083, acc 0.751391\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-17000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:33:28.959637: step 17100, loss 0.0223754, acc 0.768089\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-17100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:33:37.576608: step 17200, loss 0.022175, acc 0.766234\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-17200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:33:46.215657: step 17300, loss 0.0226481, acc 0.7718\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-17300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:33:54.783471: step 17400, loss 0.0222993, acc 0.765306\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-17400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:34:03.356237: step 17500, loss 0.0223374, acc 0.779221\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-17500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:34:11.966075: step 17600, loss 0.0223585, acc 0.769944\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-17600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:34:20.628108: step 17700, loss 0.0224034, acc 0.769944\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-17700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:34:29.209550: step 17800, loss 0.0222314, acc 0.768089\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-17800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:34:37.828961: step 17900, loss 0.0220697, acc 0.765306\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-17900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:34:46.416037: step 18000, loss 0.0219709, acc 0.769944\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-18000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:34:55.057577: step 18100, loss 0.0221225, acc 0.748609\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-18100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T08:35:03.672982: step 18200, loss 0.0217609, acc 0.776438\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-18200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:35:12.257255: step 18300, loss 0.0218217, acc 0.763451\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-18300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:35:20.827848: step 18400, loss 0.0216757, acc 0.773655\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-18400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:35:29.429038: step 18500, loss 0.0217318, acc 0.773655\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-18500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:35:38.033644: step 18600, loss 0.021682, acc 0.777366\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-18600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:35:46.615661: step 18700, loss 0.0216822, acc 0.7718\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-18700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:35:55.207212: step 18800, loss 0.0214461, acc 0.770872\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-18800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:36:03.838581: step 18900, loss 0.021494, acc 0.7718\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-18900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:36:12.437239: step 19000, loss 0.0215303, acc 0.780148\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-19000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:36:21.030502: step 19100, loss 0.0215637, acc 0.774583\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-19100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:36:29.679832: step 19200, loss 0.021372, acc 0.774583\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-19200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:36:38.261013: step 19300, loss 0.0210287, acc 0.773655\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-19300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:36:46.885213: step 19400, loss 0.0210805, acc 0.774583\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-19400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:36:55.491217: step 19500, loss 0.0211872, acc 0.765306\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-19500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:37:04.093095: step 19600, loss 0.021298, acc 0.782931\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-19600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:37:12.746606: step 19700, loss 0.0214544, acc 0.776438\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-19700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:37:21.354308: step 19800, loss 0.0212259, acc 0.776438\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-19800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:37:30.018373: step 19900, loss 0.0213973, acc 0.769017\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-19900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:37:38.607146: step 20000, loss 0.0212129, acc 0.777366\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-20000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:37:47.255830: step 20100, loss 0.0213456, acc 0.772727\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-20100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T08:37:55.871961: step 20200, loss 0.0208543, acc 0.774583\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-20200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:38:04.545574: step 20300, loss 0.0208776, acc 0.782004\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-20300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:38:13.150860: step 20400, loss 0.020944, acc 0.779221\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-20400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:38:21.806250: step 20500, loss 0.0209039, acc 0.780148\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-20500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:38:30.391117: step 20600, loss 0.0209817, acc 0.782004\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-20600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:38:38.981005: step 20700, loss 0.020982, acc 0.777366\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-20700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:38:47.627860: step 20800, loss 0.0211743, acc 0.784787\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-20800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:38:56.221660: step 20900, loss 0.0208352, acc 0.781076\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-20900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:39:04.815170: step 21000, loss 0.0205311, acc 0.78757\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-21000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:39:13.482578: step 21100, loss 0.0209131, acc 0.780148\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-21100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:39:22.081712: step 21200, loss 0.0204882, acc 0.782931\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-21200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:39:30.681853: step 21300, loss 0.0205751, acc 0.778293\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-21300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:39:39.255487: step 21400, loss 0.0204242, acc 0.772727\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-21400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:39:47.895497: step 21500, loss 0.0205452, acc 0.789425\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-21500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:39:56.532243: step 21600, loss 0.0206131, acc 0.776438\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-21600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:40:05.169801: step 21700, loss 0.0207949, acc 0.782931\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-21700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:40:13.747822: step 21800, loss 0.0204416, acc 0.783859\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-21800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:40:22.333275: step 21900, loss 0.0204184, acc 0.783859\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-21900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:40:30.925868: step 22000, loss 0.0205507, acc 0.785714\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-22000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:40:39.559864: step 22100, loss 0.0204118, acc 0.784787\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-22100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T08:40:48.123762: step 22200, loss 0.0201802, acc 0.780148\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-22200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:40:56.720669: step 22300, loss 0.0202763, acc 0.783859\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-22300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:41:05.411906: step 22400, loss 0.0203035, acc 0.78757\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-22400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:41:13.987068: step 22500, loss 0.0204576, acc 0.783859\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-22500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:41:22.633522: step 22600, loss 0.0203506, acc 0.783859\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-22600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:41:31.213205: step 22700, loss 0.0204905, acc 0.780148\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-22700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:41:39.810963: step 22800, loss 0.0202333, acc 0.794991\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-22800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:41:48.458548: step 22900, loss 0.0199978, acc 0.782931\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-22900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:41:57.021012: step 23000, loss 0.0200485, acc 0.794991\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-23000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:42:05.735973: step 23100, loss 0.0202129, acc 0.773655\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-23100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:42:14.333317: step 23200, loss 0.0200276, acc 0.786642\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-23200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:42:22.969169: step 23300, loss 0.0200739, acc 0.785714\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-23300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:42:31.643261: step 23400, loss 0.0201025, acc 0.792208\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-23400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:42:40.240556: step 23500, loss 0.0202617, acc 0.784787\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-23500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:42:48.810282: step 23600, loss 0.0201484, acc 0.788497\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-23600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:42:57.423026: step 23700, loss 0.0199774, acc 0.79128\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-23700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:43:06.107490: step 23800, loss 0.0198949, acc 0.79128\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-23800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:43:14.746697: step 23900, loss 0.0199182, acc 0.78757\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-23900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:43:23.337435: step 24000, loss 0.0196217, acc 0.78757\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-24000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:43:31.933865: step 24100, loss 0.0197143, acc 0.78757\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-24100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T08:43:40.565023: step 24200, loss 0.0198986, acc 0.79128\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-24200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:43:49.258267: step 24300, loss 0.0194647, acc 0.801484\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-24300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:43:57.840042: step 24400, loss 0.0197561, acc 0.782931\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-24400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:44:06.434427: step 24500, loss 0.0202282, acc 0.792208\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-24500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:44:15.053190: step 24600, loss 0.0199561, acc 0.793135\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-24600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:44:23.672051: step 24700, loss 0.0198195, acc 0.792208\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-24700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:44:32.361285: step 24800, loss 0.0197054, acc 0.789425\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-24800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:44:40.967917: step 24900, loss 0.0195237, acc 0.792208\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-24900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:44:49.542883: step 25000, loss 0.0197013, acc 0.794991\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-25000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:44:58.148658: step 25100, loss 0.019721, acc 0.792208\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-25100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:45:06.752930: step 25200, loss 0.0199326, acc 0.794063\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-25200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:45:15.444864: step 25300, loss 0.0196668, acc 0.788497\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-25300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:45:24.019585: step 25400, loss 0.0197643, acc 0.788497\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-25400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:45:32.594336: step 25500, loss 0.0200372, acc 0.796846\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-25500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:45:41.256071: step 25600, loss 0.0196692, acc 0.794991\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-25600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:45:49.860670: step 25700, loss 0.0191855, acc 0.794063\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-25700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:45:58.501292: step 25800, loss 0.019523, acc 0.78757\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-25800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:46:07.082218: step 25900, loss 0.0194284, acc 0.794063\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-25900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:46:15.704455: step 26000, loss 0.0196232, acc 0.794063\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-26000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:46:24.329462: step 26100, loss 0.0193707, acc 0.794991\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-26100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T08:46:32.962103: step 26200, loss 0.0194304, acc 0.798701\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-26200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:46:41.562432: step 26300, loss 0.0192464, acc 0.799629\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-26300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:46:50.161032: step 26400, loss 0.0193574, acc 0.799629\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-26400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:46:58.797113: step 26500, loss 0.0190088, acc 0.797774\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-26500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:47:07.409173: step 26600, loss 0.0191704, acc 0.792208\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-26600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:47:16.016676: step 26700, loss 0.0194104, acc 0.779221\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-26700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:47:24.660054: step 26800, loss 0.0191686, acc 0.799629\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-26800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:47:33.335662: step 26900, loss 0.0191965, acc 0.794991\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-26900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:47:41.928311: step 27000, loss 0.0190118, acc 0.797774\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-27000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:47:50.531573: step 27100, loss 0.0195463, acc 0.794991\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-27100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:47:59.157525: step 27200, loss 0.0192821, acc 0.798701\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-27200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:48:07.772353: step 27300, loss 0.0191798, acc 0.794991\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-27300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:48:16.393122: step 27400, loss 0.019268, acc 0.794991\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-27400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:48:25.032703: step 27500, loss 0.019031, acc 0.799629\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-27500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:48:33.650912: step 27600, loss 0.0193179, acc 0.788497\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-27600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:48:42.254965: step 27700, loss 0.0190742, acc 0.795918\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-27700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:48:50.861443: step 27800, loss 0.0191662, acc 0.794063\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-27800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:48:59.519444: step 27900, loss 0.0192489, acc 0.798701\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-27900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:49:08.091674: step 28000, loss 0.0194024, acc 0.800557\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-28000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:49:16.739404: step 28100, loss 0.019056, acc 0.794991\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-28100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T08:49:25.354267: step 28200, loss 0.0192398, acc 0.795918\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-28200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:49:34.037266: step 28300, loss 0.0192121, acc 0.798701\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-28300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:49:42.692969: step 28400, loss 0.0192847, acc 0.796846\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-28400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:49:51.308044: step 28500, loss 0.0188527, acc 0.794991\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-28500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:49:59.887623: step 28600, loss 0.0188368, acc 0.799629\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-28600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:50:08.575004: step 28700, loss 0.0187892, acc 0.804267\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-28700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:50:17.163006: step 28800, loss 0.0189103, acc 0.800557\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-28800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:50:25.813606: step 28900, loss 0.018995, acc 0.802412\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-28900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:50:34.428069: step 29000, loss 0.0190343, acc 0.798701\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-29000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:50:43.093304: step 29100, loss 0.0189308, acc 0.798701\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-29100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:50:51.695632: step 29200, loss 0.0189973, acc 0.799629\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-29200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:51:00.366974: step 29300, loss 0.0190775, acc 0.794991\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-29300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:51:08.961085: step 29400, loss 0.0189574, acc 0.801484\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-29400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:51:17.617588: step 29500, loss 0.0189138, acc 0.799629\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-29500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:51:26.223124: step 29600, loss 0.0189305, acc 0.801484\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-29600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:51:34.851625: step 29700, loss 0.0189024, acc 0.80334\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-29700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:51:43.427633: step 29800, loss 0.0188194, acc 0.799629\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-29800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:51:52.019056: step 29900, loss 0.0190206, acc 0.792208\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-29900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:52:00.588589: step 30000, loss 0.0188724, acc 0.802412\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-30000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:52:09.174569: step 30100, loss 0.0188127, acc 0.798701\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-30100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T08:52:17.834267: step 30200, loss 0.018377, acc 0.798701\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-30200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:52:26.392123: step 30300, loss 0.0191152, acc 0.797774\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-30300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:52:35.063483: step 30400, loss 0.0187649, acc 0.795918\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-30400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:52:43.695218: step 30500, loss 0.0187469, acc 0.804267\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-30500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:52:52.293791: step 30600, loss 0.0185588, acc 0.802412\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-30600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:53:00.882176: step 30700, loss 0.0187432, acc 0.796846\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-30700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:53:09.484496: step 30800, loss 0.018848, acc 0.799629\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-30800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:53:18.071017: step 30900, loss 0.0185854, acc 0.804267\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-30900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:53:26.656311: step 31000, loss 0.0184399, acc 0.80334\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-31000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:53:35.242368: step 31100, loss 0.0185009, acc 0.80334\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-31100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:53:43.824939: step 31200, loss 0.018579, acc 0.806122\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-31200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:53:52.478579: step 31300, loss 0.0184132, acc 0.801484\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-31300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:54:01.056111: step 31400, loss 0.0186723, acc 0.812616\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-31400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:54:09.615005: step 31500, loss 0.0184384, acc 0.799629\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-31500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:54:18.222719: step 31600, loss 0.0190272, acc 0.801484\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-31600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:54:26.792408: step 31700, loss 0.0185112, acc 0.796846\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-31700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:54:35.377449: step 31800, loss 0.0185786, acc 0.80705\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-31800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:54:43.968284: step 31900, loss 0.0183979, acc 0.799629\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-31900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:54:52.547752: step 32000, loss 0.018543, acc 0.802412\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-32000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:55:01.170087: step 32100, loss 0.0185466, acc 0.80705\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-32100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T08:55:09.749375: step 32200, loss 0.0186432, acc 0.807978\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-32200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:55:18.323429: step 32300, loss 0.0184816, acc 0.808905\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-32300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:55:26.897396: step 32400, loss 0.0184142, acc 0.802412\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-32400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:55:35.600253: step 32500, loss 0.0182443, acc 0.812616\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-32500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:55:44.234396: step 32600, loss 0.0185988, acc 0.797774\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-32600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:55:52.840344: step 32700, loss 0.018341, acc 0.804267\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-32700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:56:01.486206: step 32800, loss 0.0184116, acc 0.807978\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-32800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:56:10.072060: step 32900, loss 0.018547, acc 0.80705\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-32900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:56:18.661464: step 33000, loss 0.0185469, acc 0.804267\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-33000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:56:27.309424: step 33100, loss 0.018402, acc 0.806122\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-33100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:56:35.932710: step 33200, loss 0.0184929, acc 0.808905\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-33200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:56:44.574157: step 33300, loss 0.0184103, acc 0.806122\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-33300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:56:53.193706: step 33400, loss 0.0184399, acc 0.806122\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-33400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:57:01.866010: step 33500, loss 0.0181399, acc 0.808905\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-33500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:57:10.460020: step 33600, loss 0.0185447, acc 0.801484\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-33600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:57:19.115020: step 33700, loss 0.0182302, acc 0.806122\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-33700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:57:27.715438: step 33800, loss 0.0181614, acc 0.805195\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-33800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:57:36.375308: step 33900, loss 0.0184491, acc 0.800557\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-33900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:57:44.982535: step 34000, loss 0.0185467, acc 0.795918\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-34000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:57:53.628170: step 34100, loss 0.0188516, acc 0.805195\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-34100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T08:58:02.211591: step 34200, loss 0.018379, acc 0.800557\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-34200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:58:10.873007: step 34300, loss 0.0185913, acc 0.79128\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-34300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:58:19.486232: step 34400, loss 0.0181524, acc 0.806122\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-34400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:58:28.151484: step 34500, loss 0.0185715, acc 0.804267\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-34500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:58:36.763006: step 34600, loss 0.0181227, acc 0.807978\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-34600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:58:45.411381: step 34700, loss 0.0179798, acc 0.800557\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-34700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:58:54.029568: step 34800, loss 0.017959, acc 0.814471\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-34800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:59:02.697694: step 34900, loss 0.0182062, acc 0.804267\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-34900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:59:11.319443: step 35000, loss 0.018192, acc 0.809833\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-35000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:59:19.956155: step 35100, loss 0.0178098, acc 0.805195\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-35100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:59:28.534030: step 35200, loss 0.0181448, acc 0.804267\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-35200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:59:37.129826: step 35300, loss 0.0181002, acc 0.807978\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-35300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:59:45.788611: step 35400, loss 0.0182407, acc 0.811688\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-35400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T08:59:54.368385: step 35500, loss 0.0184665, acc 0.807978\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-35500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:00:02.987268: step 35600, loss 0.0180839, acc 0.798701\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-35600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:00:11.613598: step 35700, loss 0.0183193, acc 0.804267\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-35700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:00:20.211227: step 35800, loss 0.0180894, acc 0.813544\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-35800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:00:28.819141: step 35900, loss 0.0180632, acc 0.809833\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-35900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:00:37.426971: step 36000, loss 0.0180338, acc 0.807978\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-36000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:00:46.041935: step 36100, loss 0.0184829, acc 0.80705\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-36100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T09:00:54.649782: step 36200, loss 0.0180011, acc 0.809833\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-36200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:01:03.266637: step 36300, loss 0.0184893, acc 0.800557\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-36300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:01:11.913149: step 36400, loss 0.0181625, acc 0.820965\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-36400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:01:20.494589: step 36500, loss 0.017888, acc 0.809833\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-36500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:01:29.084487: step 36600, loss 0.0180372, acc 0.806122\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-36600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:01:37.728231: step 36700, loss 0.0182111, acc 0.809833\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-36700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:01:46.326458: step 36800, loss 0.017716, acc 0.805195\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-36800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:01:54.976311: step 36900, loss 0.0180394, acc 0.80334\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-36900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:02:03.586493: step 37000, loss 0.0180863, acc 0.807978\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-37000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:02:12.222172: step 37100, loss 0.0181647, acc 0.807978\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-37100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:02:20.832064: step 37200, loss 0.0177606, acc 0.804267\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-37200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:02:29.434455: step 37300, loss 0.0178434, acc 0.80705\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-37300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:02:38.066495: step 37400, loss 0.0178575, acc 0.813544\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-37400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:02:46.662366: step 37500, loss 0.0181472, acc 0.808905\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-37500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:02:55.318583: step 37600, loss 0.0178146, acc 0.817254\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-37600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:03:03.912651: step 37700, loss 0.017759, acc 0.809833\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-37700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:03:12.563491: step 37800, loss 0.0176335, acc 0.80705\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-37800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:03:21.149653: step 37900, loss 0.0179461, acc 0.809833\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-37900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:03:29.739501: step 38000, loss 0.0180256, acc 0.801484\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-38000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:03:38.411814: step 38100, loss 0.018165, acc 0.808905\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-38100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T09:03:47.056853: step 38200, loss 0.0181262, acc 0.806122\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-38200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:03:55.662515: step 38300, loss 0.0182412, acc 0.807978\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-38300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:04:04.263575: step 38400, loss 0.0178129, acc 0.815399\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-38400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:04:12.922162: step 38500, loss 0.0180384, acc 0.809833\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-38500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:04:21.519961: step 38600, loss 0.0176557, acc 0.811688\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-38600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:04:30.159409: step 38700, loss 0.0179659, acc 0.810761\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-38700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:04:38.756431: step 38800, loss 0.0177408, acc 0.811688\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-38800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:04:47.355178: step 38900, loss 0.017834, acc 0.813544\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-38900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:04:55.978174: step 39000, loss 0.0179996, acc 0.806122\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-39000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:05:04.574977: step 39100, loss 0.017598, acc 0.807978\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-39100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:05:13.223365: step 39200, loss 0.0175765, acc 0.815399\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-39200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:05:21.809615: step 39300, loss 0.0181354, acc 0.809833\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-39300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:05:30.390210: step 39400, loss 0.0176487, acc 0.811688\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-39400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:05:39.012074: step 39500, loss 0.0178581, acc 0.807978\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-39500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:05:47.609887: step 39600, loss 0.0178554, acc 0.811688\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-39600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:05:56.187012: step 39700, loss 0.017496, acc 0.806122\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-39700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:06:04.835879: step 39800, loss 0.0175286, acc 0.813544\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-39800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:06:13.456591: step 39900, loss 0.0177672, acc 0.809833\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-39900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:06:22.073765: step 40000, loss 0.0180201, acc 0.813544\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-40000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:06:30.709316: step 40100, loss 0.0175351, acc 0.818182\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-40100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T09:06:39.278465: step 40200, loss 0.0175166, acc 0.808905\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-40200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:06:47.892898: step 40300, loss 0.0177387, acc 0.814471\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-40300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:06:56.497800: step 40400, loss 0.0179003, acc 0.812616\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-40400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:07:05.075045: step 40500, loss 0.0178415, acc 0.805195\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-40500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:07:13.718243: step 40600, loss 0.0178848, acc 0.808905\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-40600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:07:22.338489: step 40700, loss 0.0180347, acc 0.811688\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-40700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:07:30.983889: step 40800, loss 0.0177828, acc 0.811688\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-40800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:07:39.576887: step 40900, loss 0.01762, acc 0.814471\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-40900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:07:48.188869: step 41000, loss 0.0178001, acc 0.812616\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-41000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:07:56.817163: step 41100, loss 0.0175686, acc 0.815399\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-41100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:08:05.424037: step 41200, loss 0.0179661, acc 0.813544\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-41200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:08:14.070017: step 41300, loss 0.0174194, acc 0.819109\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-41300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:08:22.662876: step 41400, loss 0.0177708, acc 0.811688\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-41400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:08:31.252468: step 41500, loss 0.0175659, acc 0.813544\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-41500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:08:39.852422: step 41600, loss 0.0178992, acc 0.811688\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-41600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:08:48.434354: step 41700, loss 0.0179384, acc 0.815399\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-41700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:08:57.017131: step 41800, loss 0.0173584, acc 0.817254\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-41800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:09:05.635921: step 41900, loss 0.0177548, acc 0.808905\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-41900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:09:14.225583: step 42000, loss 0.0178029, acc 0.816327\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-42000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:09:22.828053: step 42100, loss 0.0178634, acc 0.816327\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-42100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T09:09:31.405694: step 42200, loss 0.0176977, acc 0.813544\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-42200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:09:40.035107: step 42300, loss 0.0173578, acc 0.818182\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-42300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:09:48.640883: step 42400, loss 0.0176193, acc 0.812616\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-42400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:09:57.212272: step 42500, loss 0.0176587, acc 0.808905\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-42500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:10:05.860706: step 42600, loss 0.0177151, acc 0.810761\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-42600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:10:14.473032: step 42700, loss 0.017869, acc 0.811688\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-42700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:10:23.075492: step 42800, loss 0.0178008, acc 0.810761\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-42800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:10:31.699119: step 42900, loss 0.0180982, acc 0.813544\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-42900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:10:40.299090: step 43000, loss 0.0177202, acc 0.802412\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-43000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:10:48.944712: step 43100, loss 0.0176925, acc 0.810761\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-43100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:10:57.537787: step 43200, loss 0.0178823, acc 0.811688\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-43200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:11:06.164619: step 43300, loss 0.0176706, acc 0.814471\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-43300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:11:14.757885: step 43400, loss 0.0176133, acc 0.813544\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-43400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:11:23.342339: step 43500, loss 0.0176409, acc 0.812616\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-43500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:11:31.961140: step 43600, loss 0.0174663, acc 0.823748\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-43600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:11:40.527315: step 43700, loss 0.0175653, acc 0.808905\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-43700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:11:49.127853: step 43800, loss 0.0178857, acc 0.815399\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-43800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:11:57.723752: step 43900, loss 0.0175628, acc 0.810761\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-43900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:12:06.339708: step 44000, loss 0.0178523, acc 0.812616\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-44000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:12:14.952952: step 44100, loss 0.0175019, acc 0.813544\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-44100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T09:12:23.560672: step 44200, loss 0.0177477, acc 0.812616\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-44200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:12:32.207693: step 44300, loss 0.0179951, acc 0.819109\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-44300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:12:40.782305: step 44400, loss 0.0177735, acc 0.80705\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-44400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:12:49.424189: step 44500, loss 0.0175581, acc 0.813544\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-44500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:12:58.006005: step 44600, loss 0.0174264, acc 0.820965\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-44600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:13:06.600918: step 44700, loss 0.0177493, acc 0.817254\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-44700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:13:15.239232: step 44800, loss 0.0175845, acc 0.814471\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-44800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:13:23.829519: step 44900, loss 0.0177879, acc 0.815399\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-44900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:13:32.415003: step 45000, loss 0.0174502, acc 0.813544\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-45000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:13:41.145778: step 45100, loss 0.0176209, acc 0.815399\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-45100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:13:49.795558: step 45200, loss 0.0177469, acc 0.814471\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-45200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:13:58.371660: step 45300, loss 0.017637, acc 0.815399\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-45300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:14:07.006281: step 45400, loss 0.0175061, acc 0.810761\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-45400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:14:15.604424: step 45500, loss 0.0179903, acc 0.810761\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-45500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:14:24.209306: step 45600, loss 0.0173691, acc 0.816327\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-45600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:14:32.882097: step 45700, loss 0.01724, acc 0.82282\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-45700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:14:41.455227: step 45800, loss 0.0174562, acc 0.82282\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-45800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:14:50.128405: step 45900, loss 0.0175313, acc 0.814471\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-45900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:14:58.741305: step 46000, loss 0.0175097, acc 0.819109\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-46000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:15:07.380930: step 46100, loss 0.0175674, acc 0.819109\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-46100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T09:15:15.976044: step 46200, loss 0.0173271, acc 0.815399\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-46200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:15:24.607640: step 46300, loss 0.0172726, acc 0.809833\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-46300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:15:33.217470: step 46400, loss 0.0174674, acc 0.818182\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-46400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:15:41.839287: step 46500, loss 0.017418, acc 0.813544\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-46500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:15:50.422624: step 46600, loss 0.0172307, acc 0.818182\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-46600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:15:59.045754: step 46700, loss 0.017358, acc 0.808905\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-46700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:16:07.658454: step 46800, loss 0.0174785, acc 0.809833\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-46800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:16:16.242094: step 46900, loss 0.0173464, acc 0.814471\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-46900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:16:24.846635: step 47000, loss 0.0174448, acc 0.817254\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-47000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:16:33.471587: step 47100, loss 0.0174259, acc 0.820037\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-47100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:16:42.048895: step 47200, loss 0.017582, acc 0.820037\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-47200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:16:50.672981: step 47300, loss 0.0173509, acc 0.810761\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-47300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:16:59.301018: step 47400, loss 0.0176039, acc 0.812616\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-47400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:17:07.933302: step 47500, loss 0.0175449, acc 0.817254\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-47500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:17:16.575958: step 47600, loss 0.017523, acc 0.812616\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-47600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:17:25.189496: step 47700, loss 0.0172337, acc 0.817254\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-47700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:17:33.776237: step 47800, loss 0.0175584, acc 0.819109\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-47800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:17:42.417231: step 47900, loss 0.0173077, acc 0.813544\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-47900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:17:51.026680: step 48000, loss 0.0176864, acc 0.832096\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-48000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:17:59.656491: step 48100, loss 0.0174477, acc 0.818182\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-48100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T09:18:08.291171: step 48200, loss 0.0176237, acc 0.811688\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-48200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:18:16.948153: step 48300, loss 0.0173902, acc 0.814471\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-48300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:18:25.528243: step 48400, loss 0.0176631, acc 0.810761\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-48400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:18:34.124871: step 48500, loss 0.0173663, acc 0.820037\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-48500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:18:42.781841: step 48600, loss 0.0176288, acc 0.824675\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-48600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:18:51.358933: step 48700, loss 0.017576, acc 0.813544\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-48700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:19:00.027541: step 48800, loss 0.017461, acc 0.820037\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-48800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:19:08.635302: step 48900, loss 0.0172886, acc 0.818182\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-48900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:19:17.291329: step 49000, loss 0.0172549, acc 0.824675\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-49000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:19:25.888494: step 49100, loss 0.0171972, acc 0.817254\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-49100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:19:34.570262: step 49200, loss 0.0174202, acc 0.820965\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-49200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:19:43.133255: step 49300, loss 0.0175364, acc 0.811688\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-49300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:19:51.781191: step 49400, loss 0.0173891, acc 0.819109\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-49400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:20:00.394584: step 49500, loss 0.0175021, acc 0.820965\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-49500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:20:09.005297: step 49600, loss 0.0173322, acc 0.816327\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-49600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:20:17.620326: step 49700, loss 0.0175696, acc 0.821892\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-49700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:20:26.233101: step 49800, loss 0.0174467, acc 0.82282\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-49800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:20:34.886070: step 49900, loss 0.0175091, acc 0.813544\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-49900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:20:43.493204: step 50000, loss 0.0172456, acc 0.819109\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-50000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:20:52.136950: step 50100, loss 0.0174529, acc 0.814471\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-50100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T09:21:00.716251: step 50200, loss 0.017222, acc 0.816327\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-50200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:21:09.317501: step 50300, loss 0.017547, acc 0.820965\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-50300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:21:17.920887: step 50400, loss 0.0170165, acc 0.824675\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-50400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:21:26.504136: step 50500, loss 0.0174723, acc 0.818182\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-50500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:21:35.099396: step 50600, loss 0.0172851, acc 0.818182\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-50600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:21:43.693584: step 50700, loss 0.0172925, acc 0.821892\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-50700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:21:52.284471: step 50800, loss 0.0176048, acc 0.807978\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-50800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:22:00.885872: step 50900, loss 0.0170472, acc 0.813544\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-50900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:22:09.493047: step 51000, loss 0.0171986, acc 0.829314\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-51000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:22:18.127614: step 51100, loss 0.0172162, acc 0.813544\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-51100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:22:26.752078: step 51200, loss 0.0171432, acc 0.814471\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-51200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:22:35.400879: step 51300, loss 0.0171338, acc 0.817254\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-51300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:22:44.020324: step 51400, loss 0.0170451, acc 0.818182\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-51400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:22:52.671448: step 51500, loss 0.0173353, acc 0.818182\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-51500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:23:01.314969: step 51600, loss 0.0177933, acc 0.816327\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-51600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:23:09.933014: step 51700, loss 0.0178183, acc 0.814471\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-51700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:23:18.515318: step 51800, loss 0.0174326, acc 0.824675\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-51800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:23:27.170270: step 51900, loss 0.0173657, acc 0.815399\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-51900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:23:35.766055: step 52000, loss 0.0174935, acc 0.823748\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-52000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:23:44.400981: step 52100, loss 0.0172797, acc 0.82282\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-52100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T09:23:52.992621: step 52200, loss 0.0171168, acc 0.820037\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-52200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:24:01.595953: step 52300, loss 0.0175571, acc 0.814471\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-52300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:24:10.290181: step 52400, loss 0.0172791, acc 0.815399\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-52400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:24:18.954060: step 52500, loss 0.0171613, acc 0.813544\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-52500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:24:27.549700: step 52600, loss 0.0175247, acc 0.820037\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-52600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:24:36.198280: step 52700, loss 0.0175764, acc 0.820037\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-52700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:24:44.791542: step 52800, loss 0.0179383, acc 0.812616\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-52800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:24:53.364309: step 52900, loss 0.0170539, acc 0.808905\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-52900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:25:02.007807: step 53000, loss 0.0170575, acc 0.816327\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-53000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:25:10.596870: step 53100, loss 0.0172462, acc 0.812616\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-53100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:25:19.214522: step 53200, loss 0.016984, acc 0.82282\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-53200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:25:27.858181: step 53300, loss 0.0174539, acc 0.816327\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-53300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:25:36.475521: step 53400, loss 0.017477, acc 0.823748\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-53400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:25:45.114167: step 53500, loss 0.0176996, acc 0.814471\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-53500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:25:53.713618: step 53600, loss 0.0171211, acc 0.815399\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-53600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:26:02.355168: step 53700, loss 0.0169763, acc 0.827458\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-53700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:26:10.972193: step 53800, loss 0.017507, acc 0.818182\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-53800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:26:19.625442: step 53900, loss 0.0173378, acc 0.824675\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-53900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:26:28.234058: step 54000, loss 0.0174755, acc 0.814471\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-54000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:26:36.965876: step 54100, loss 0.0172403, acc 0.817254\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-54100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T09:26:45.605056: step 54200, loss 0.0177723, acc 0.819109\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-54200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:26:54.193578: step 54300, loss 0.0176262, acc 0.819109\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-54300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:27:02.842153: step 54400, loss 0.017187, acc 0.828386\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-54400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:27:11.437876: step 54500, loss 0.0174483, acc 0.816327\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-54500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:27:20.068899: step 54600, loss 0.0171439, acc 0.828386\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-54600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:27:28.697109: step 54700, loss 0.0173067, acc 0.810761\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-54700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:27:37.330063: step 54800, loss 0.0176187, acc 0.812616\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-54800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:27:45.941719: step 54900, loss 0.0172652, acc 0.817254\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-54900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:27:54.552367: step 55000, loss 0.0172199, acc 0.823748\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-55000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:28:03.214149: step 55100, loss 0.0171929, acc 0.824675\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-55100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:28:11.816101: step 55200, loss 0.0171509, acc 0.819109\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-55200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:28:20.467778: step 55300, loss 0.0169563, acc 0.82282\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-55300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:28:29.061318: step 55400, loss 0.0174539, acc 0.821892\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-55400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:28:37.697924: step 55500, loss 0.0170879, acc 0.816327\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-55500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:28:46.332703: step 55600, loss 0.0170476, acc 0.815399\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-55600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:28:54.975042: step 55700, loss 0.0174978, acc 0.818182\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-55700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:29:03.561717: step 55800, loss 0.0174663, acc 0.820965\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-55800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:29:12.169561: step 55900, loss 0.0173698, acc 0.819109\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-55900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:29:20.731754: step 56000, loss 0.0174193, acc 0.827458\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-56000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:29:29.351303: step 56100, loss 0.0174057, acc 0.809833\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-56100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T09:29:37.936412: step 56200, loss 0.0172467, acc 0.826531\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-56200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:29:46.539830: step 56300, loss 0.016963, acc 0.823748\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-56300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:29:55.126993: step 56400, loss 0.0170927, acc 0.82282\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-56400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:30:03.748181: step 56500, loss 0.0175176, acc 0.826531\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-56500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:30:12.346608: step 56600, loss 0.0170828, acc 0.821892\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-56600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:30:20.953988: step 56700, loss 0.0172314, acc 0.821892\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-56700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:30:29.561934: step 56800, loss 0.0172071, acc 0.825603\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-56800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:30:38.200353: step 56900, loss 0.0176048, acc 0.818182\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-56900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:30:46.805121: step 57000, loss 0.0175499, acc 0.818182\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-57000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:30:55.440748: step 57100, loss 0.0176042, acc 0.835807\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-57100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:31:04.039192: step 57200, loss 0.0174986, acc 0.818182\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-57200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:31:12.652566: step 57300, loss 0.0173982, acc 0.820965\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-57300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:31:21.297070: step 57400, loss 0.0174179, acc 0.814471\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-57400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:31:29.904757: step 57500, loss 0.0171194, acc 0.811688\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-57500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:31:38.582568: step 57600, loss 0.0174574, acc 0.82282\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-57600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:31:47.203406: step 57700, loss 0.0170341, acc 0.826531\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-57700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:31:55.820369: step 57800, loss 0.0172811, acc 0.823748\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-57800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:32:04.421706: step 57900, loss 0.0173868, acc 0.820965\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-57900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:32:13.049594: step 58000, loss 0.0172117, acc 0.821892\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-58000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:32:21.664234: step 58100, loss 0.0170162, acc 0.818182\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-58100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T09:32:30.329801: step 58200, loss 0.0174098, acc 0.829314\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-58200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:32:38.962301: step 58300, loss 0.0171253, acc 0.820965\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-58300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:32:47.608063: step 58400, loss 0.0177403, acc 0.821892\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-58400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:32:56.233434: step 58500, loss 0.0174924, acc 0.820037\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-58500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:33:04.859145: step 58600, loss 0.0172626, acc 0.818182\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-58600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:33:13.480992: step 58700, loss 0.0171458, acc 0.825603\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-58700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:33:22.136209: step 58800, loss 0.0177543, acc 0.815399\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-58800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:33:30.751221: step 58900, loss 0.0174537, acc 0.82282\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-58900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:33:39.400596: step 59000, loss 0.0169507, acc 0.833024\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-59000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:33:48.040647: step 59100, loss 0.0173635, acc 0.824675\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-59100\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:33:56.635590: step 59200, loss 0.0174566, acc 0.820965\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-59200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:34:05.230014: step 59300, loss 0.0171184, acc 0.821892\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-59300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:34:13.851225: step 59400, loss 0.0174975, acc 0.824675\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-59400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:34:22.471950: step 59500, loss 0.0171507, acc 0.817254\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-59500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:34:31.098155: step 59600, loss 0.0174642, acc 0.817254\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-59600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:34:39.719780: step 59700, loss 0.0175737, acc 0.818182\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-59700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:34:48.307993: step 59800, loss 0.0173236, acc 0.817254\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-59800\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:34:56.980431: step 59900, loss 0.0170343, acc 0.831169\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-59900\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:35:05.580210: step 60000, loss 0.0172022, acc 0.821892\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-60000\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:35:14.216091: step 60100, loss 0.0171401, acc 0.824675\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-60100\n",
            "\n",
            "\n",
            "Evaluation:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2018-05-28T09:35:22.848960: step 60200, loss 0.0174381, acc 0.813544\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-60200\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:35:31.528453: step 60300, loss 0.0174438, acc 0.827458\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-60300\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:35:40.237615: step 60400, loss 0.0175681, acc 0.820037\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-60400\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:35:48.883799: step 60500, loss 0.0174548, acc 0.816327\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-60500\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:35:57.482146: step 60600, loss 0.0175334, acc 0.821892\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-60600\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:36:06.065042: step 60700, loss 0.0173046, acc 0.820037\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-60700\n",
            "\n",
            "\n",
            "Evaluation:\n",
            "2018-05-28T09:36:14.711386: step 60800, loss 0.0176777, acc 0.818182\n",
            "\n",
            "Saved model checkpoint to /content/runs/1527494908/checkpoints/models-60800\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "SystemExit",
          "evalue": "ignored",
          "traceback": [
            "An exception has occurred, use %tb to see the full traceback.\n",
            "\u001b[0;31mSystemExit\u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2890: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n",
            "  warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "uhGY_NQrU81k",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Tensorboard\n"
      ]
    },
    {
      "metadata": {
        "id": "ER5uQaPRoVsI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "587e4fb7-cfe0-4862-f7c3-fd33d48ff658"
      },
      "cell_type": "code",
      "source": [
        "LOG_DIR = '/content/runs/1526466096/summaries/train'\n",
        "get_ipython().system_raw(\n",
        "    'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &'\n",
        "    .format(LOG_DIR)\n",
        ")\n",
        "! wget -nc https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
        "! unzip -n ngrok-stable-linux-amd64.zip\n",
        "\n",
        "\n",
        "get_ipython().system_raw('./ngrok http 6006 &')\n",
        "! curl -s http://localhost:4040/api/tunnels | python3 -c \\\n",
        "    \"import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])\"\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "File ‘ngrok-stable-linux-amd64.zip’ already there; not retrieving.\r\n",
            "\n",
            "Archive:  ngrok-stable-linux-amd64.zip\n",
            "http://0f8ef942.ngrok.io\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "v8Q9sJXm6Fic",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "! rm runs.tar"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FbBQVUKdwPI1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "outputId": "f1f50afd-a8f7-4206-9afc-5244ebba8252"
      },
      "cell_type": "code",
      "source": [
        "! tar -cvf runs.tar /content/runs"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tar: Removing leading `/' from member names\r\n",
            "/content/runs/\r\n",
            "/content/runs/1527083462/\r\n",
            "/content/runs/1527083462/projector_metadata.tsv\n",
            "/content/runs/1527083462/checkpoints/\n",
            "/content/runs/1527083462/checkpoints/checkpoint\n",
            "/content/runs/1527083462/checkpoints/models-51200.data-00000-of-00001\n",
            "/content/runs/1527083462/checkpoints/models-51300.meta\n",
            "/content/runs/1527083462/checkpoints/models-51400.data-00000-of-00001\n",
            "/content/runs/1527083462/checkpoints/models-51600.data-00000-of-00001\n",
            "/content/runs/1527083462/checkpoints/models-51600.index\n",
            "/content/runs/1527083462/checkpoints/models-51500.data-00000-of-00001\n",
            "/content/runs/1527083462/checkpoints/models-51500.index\n",
            "/content/runs/1527083462/checkpoints/models-51300.data-00000-of-00001\n",
            "/content/runs/1527083462/checkpoints/models-51300.index\n",
            "/content/runs/1527083462/checkpoints/models-51400.meta\n",
            "/content/runs/1527083462/checkpoints/models-51500.meta\n",
            "/content/runs/1527083462/checkpoints/models-51400.index\n",
            "/content/runs/1527083462/checkpoints/models-51200.index\n",
            "/content/runs/1527083462/checkpoints/models-51200.meta\n",
            "/content/runs/1527083462/checkpoints/models-51600.meta\n",
            "/content/runs/1527083462/summaries/\n",
            "/content/runs/1527083462/summaries/dev/\n",
            "/content/runs/1527083462/summaries/dev/projector_config.pbtxt\n",
            "/content/runs/1527083462/summaries/dev/events.out.tfevents.1527083462.226e5c45d9c4\n",
            "/content/runs/1527083462/summaries/train/\n",
            "/content/runs/1527083462/summaries/train/events.out.tfevents.1527083462.226e5c45d9c4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "XCbzm3flbSrG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YZxDvjd-wSpY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b1ee62e6-8290-4469-dfe7-027af8f25bcd"
      },
      "cell_type": "code",
      "source": [
        "# First, we need to set our project. Replace the assignment below\n",
        "# with your project ID.\n",
        "project_id = 'ahsoka-1229'\n",
        "!gcloud config set project {project_id}"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Updated property [core/project].\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "RIGsOVeex1vo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "645782a6-8819-4ddf-ac94-2f3e28d36f60"
      },
      "cell_type": "code",
      "source": [
        "import uuid\n",
        "\n",
        "# Make a unique bucket to which we'll upload the file.\n",
        "# (GCS buckets are part of a single global namespace.)\n",
        "timestamp = str(int(time.time()))\n",
        "uuid = str(uuid.uuid1())\n",
        "bucket_name = 'colab-sample-bucket-' + timestamp\n",
        "\n",
        "# Full reference: https://cloud.google.com/storage/docs/gsutil/commands/mb\n",
        "!gsutil mb gs://{bucket_name}"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Creating gs://colab-sample-bucket-1527087897/...\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "1AJ-w_sWx7D4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "68529352-9cff-45f2-b84d-bdb9a09ecc35"
      },
      "cell_type": "code",
      "source": [
        "# Copy the file to our new bucket.\n",
        "# Full reference: https://cloud.google.com/storage/docs/gsutil/commands/cp\n",
        "!gsutil cp runs.tar gs://{bucket_name}/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Copying file://runs.tar [Content-Type=application/x-tar]...\r\n",
            "/ [0 files][    0.0 B/345.8 MiB]                                                \r==> NOTE: You are uploading one or more large file(s), which would run\r\n",
            "significantly faster if you enable parallel composite uploads. This\r\n",
            "feature can be enabled by editing the\r\n",
            "\"parallel_composite_upload_threshold\" value in your .boto\r\n",
            "configuration file. However, note that if you do this large files will\r\n",
            "be uploaded as `composite objects\r\n",
            "<https://cloud.google.com/storage/docs/composite-objects>`_,which\r\n",
            "means that any user who downloads such objects will need to have a\r\n",
            "compiled crcmod installed (see \"gsutil help crcmod\"). This is because\r\n",
            "without a compiled crcmod, computing checksums on composite objects is\r\n",
            "so slow that gsutil disables downloads of composite objects.\r\n",
            "\n",
            "|\n",
            "Operation completed over 1 objects/345.8 MiB.                                    \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "lm9bCLKFyJAF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}